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The Latest Innovation in Data Quality - Data Observability

Mar 22, 2024

In the rapidly evolving digital era, data has accelerated in its value and importance to powering accurate decision-enablement for businesses across the globe. However, the sheer volume, complexity, and diversity of data types have introduced significant challenges in ensuring its accuracy, availability, and reliability. This is where Data Observability, leveraging AI, has rapidly moved to the forefront as a critical automated accelerator for organizations aiming to maintain high-quality data assets. Data Observability is defined as an extension of the concept of observability in engineering, which focuses on proactive monitoring, tracking, and triaging issues within a system. It encompasses a set of ai-enabled automation tools and practices designed to help data teams proactively monitor data pipelines, detect and diagnose issues, and ensure data quality across the entire data lifecycle. By providing comprehensive visibility into the health of data moving through pipelines into curated data assets, Data Observability enables real-time identification, and in some cases, on-the-fly remediation of inaccuracies, inconsistencies, and other anomalies that could impact business outcomes driven by decisions that are enabled by analytics based on curated data assets. The automation-enabled Data Observability approach offers a proactive approach to data quality management. Traditional data quality management strategies often rely on reactive, and highly human-centric measures, addressing issues only after they have manifested into larger problems, sometimes not identified until a decision maker questions the validity of insights based on underlying data assets. With Data Observability, however, data management teams are empowered with the tools to predict and prevent issues before they escalate, thus maintaining the integrity and reliability of data. In addition, Data Observability facilitates better collaboration among data engineers, scientists, and analysts by providing a common framework and language for understanding data health. This enhanced communication leads to more efficient problem-solving and decision-making processes, further bolstering an organization's data-driven capabilities. Top Data Observability Tools (One Man’s Opinion) Several cutting-edge tools have emerged in the marketplace, each offering unique features to tackle the challenges of Data Observability. Here are some of the top tools currently leading the way: Monte Carlo is well known for its comprehensive approach to Data Observability, Monte Carlo helps organizations automatically detect and remediate data issues. It offers features like end-to-end data lineage, anomaly detection, and real-time alerts, making it a powerful ally in proactively ensuring data integrity. DataDog is widely recognized for its cloud monitoring solutions, DataDog has expanded its offerings to include Data Observability. Its capabilities allow for the monitoring of data pipelines, databases, and data lakes, providing insights into performance issues and operational inefficiencies. Databand provides a proactive Data Observability platform designed to identify issues in data pipelines before they affect downstream processes. It provides detailed observability into data quality, pipeline health, and operational metrics, facilitating quicker issue resolution and overall operational health. Bigeye focuses on automating data quality monitoring and anomaly detection. It allows data teams to set custom metrics for data quality, receive alerts for potential issues, and track data health over time, improving overall data quality in large and complex data ecosystems. Acceldata is a multi-dimensional Data Observability platform encompassing data quality, performance, and consumption observability. It is designed to provide data teams with insights into how data is being used, its quality, and the performance of data systems, enabling more informed decision-making and strategic planning. Concluding Thoughts As data continues to elevate in importance for organizations, the role of Data Observability in proactively ensuring its reliability and integrity cannot be overstated. With the advent tools like those mentioned above, businesses are better equipped than ever to navigate the complexities of modern data systems while proactively ensuring integrity of the data assets contained therein. That said, I must offer a word of caution – buyer beware! This lane of technology is fairly new and rapidly evolving. Many of our clients have expressed concerns, and we agree, that Data Observability tools are diverse in their feature set, and relatively expensive. Meaning it is not easy to make an Apples-to-Apples comparison of competing products. So, here is where the old adage of “wisdom is found in a multitude of counselors” rings very true. At New Era we are leading the way in helping our clients replace confusion with clarity as they chart a course to embrace the opportunity created by these tools, while minimizing the risk of making a bad decision along the way. If you would like to explore how we can help your organization navigate these choppy waters feel free to reach out!

Wearables are changing healthcare: Impact for our most vulnerable

Aug 15, 2022

When it comes to understanding how wearables are changing healthcare, consumer brands serve as a solid leading indicator. Popularized by brands like Apple Watch, FitBit, and Garmin, the global wearable healthcare market was estimated at $16.2 billion in 2021, and is projected to double in the next five years. Healthcare wearables in daily life Although most users rely on healthcare wearables to check texts during spin class or crush their friends’ daily step records, an increasing number of users rely on smartwatches and other medical wearables for life-saving medical information. As the technology evolves, healthcare wearables can now give minute-by-minute EKG readings, monitor blood sugar, check oxygenation levels, and help people use real-time data to manage their health while they go about their regular activities. Wearables also deliver oversight and peace of mind to caregivers, as when diabetic children wear devices that monitor insulin and food intake and link to mobile apps monitored by their parents. These breakthroughs allow patients of all ages more autonomy while providing reassurance to caregivers that the person is safe. Learn more about what wearable devices make possible >> Healthcare wearables in long-term care settings Long-term care presents a gap between that at-home monitoring scenario and the tech-saturated acute care space of a hospital or clinic. Historically understaffed, nursing homes and long-term care facilities struggle with high turnover, increasing rates of preventable errors, and unnecessary escalation of avoidable medical events. In addition to the impact on the patient and their loved ones, these realities impact the facility itself through lower reimbursement rates and increased cost of care. A 2021 American Healthcare Association and the National Center for Assisted Living survey on staffing in these facilities showed that 99% of nursing homes and 96% of assisted-living facilities face a staffing shortage. Harvard University professor David Grabowski says the pandemic only worsened that already critical situation. He notes, “We’ve overlooked and undervalued this workforce for a long time and now we’re at a full-blown crisis…We’re in a crisis on top of a crisis.” Ensuring the right level of care for high-risk and elderly patients amidst staffing constraints formed a critical use case for transformation. Healthcare wearables emerged as a leading option that would give staff the ability to monitor more patients, get notifications when care is needed, and escalate when necessary. Overcoming obstacles to adoption Implementing a program for wearable devices in nursing homes introduced more stringent requirements than consumer wearables, including: Privacy protection: Patient medical information, covered under HIPAA, requires more protection than off-the-shelf iOS and Android systems offer. Usability concerns: Patients under care in nursing homes and long-term care facilities often lack experience with technology, and/or the dexterity to manage new devices. Cost considerations: In addition to the cost of patient wearables and devices allowing nursing staff to monitor and communicate alerts, facilities must also invest in secure data infrastructure and information architecture beyond standard integrations in market-ready smartwatches. Creating a targeted solution Realizing that nursing home and long-term care facilities faced unique barriers to implementing wearable devices, BioLink Systems set out to create a solution. Initially, the company devised a device that could be attached to an adult brief to monitor urination levels and body position. However, early issues with the prototype limited production scalability. Fusion worked with BioLink to architect a cloud-based IoT solution that uses machine learning to exceed the company’s initial vision. Designed with a minimalist aesthetic and user experience to fit the target demographic, the BioLink bracelet and adult brief wearables: Meet HIPAA requirements Monitor patient fluids Track patient vital signs Alert nursing staff when patient vitals fall outside their customized range Escalate alerts if patients are not attended within an allotted timeframe Initial testing and rollouts in nursing homes delivered immediate results, including: Improved patient care Decreased response times Fewer avoidable events such as medication errors Decreased escalation of care level, including hospitalizations Improved oversight Increased compliance with state, federal, and agency regulations Better experiences for the patient and their loved ones Learn more about how BioLink’s wearables are changing healthcare >> What’s next for wearable healthcare devices As facilities gather more data from using these devices, the machine learning algorithm BioLink and Fusion designed will continue to refine unique vital sign ranges for each patient, resulting in more targeted care. Future iterations of the BioLink device will integrate that information with the patient’s electronic medical record, enabling further customization of care. While each device starts with a baseline for normal with each of these vital signs, the more data that is collected, the better the facility can care for the patient. For example, if a patient’s oxygen level is continuously high, the device eventually creates a new threshold for that patient’s vitals and only sends notifications accordingly. Especially with the elderly population, there are many people that can’t communicate what they need or when they feel a certain way. There are endless possibilities for being able to provide better care under these circumstances. With options like dehydration sensors, nursing care staff is better able to not only bring water to patients but ensure that they are actually consuming it. The more variables, the better Ultimately, the more variables, the better the information — resulting in better care and better outcomes. The correlation and combination of all the data from a patient can detect changes and allow for more timely, preventative care. And the more information included, the better the insights from the algorithm. With the right information, staff can prevent different medical events by predicting problems and eventually creating better remedies and treatments to avoid costly medical interventions or catastrophic incidents. As industry leaders and healthcare facilities see the impact of devices like BioLink’s bracelets, we expect to see greater adoption of healthcare wearables to elevate patient care, reduce facility costs, and find operational efficiencies even during times of staffing crises. With the right technology and innovation, we can change outcomes and save lives — with a wristband.

Accelerating digital transformation in finance and banking

Aug 11, 2022

From online transactions to mobile payment apps like Venmo, today's consumers increasingly look for digital access to funds — and they expect a seamless experience. As customer expectations and the economy continue to evolve, digital transformation in finance and banking needs to keep pace. Banking culture hasn’t always kept up with what digital customers are actually looking for. The World Banking Report 2021 reported that, “Despite being vocal about improving the customer experience, the banking industry’s delivery of the key components of a strong customer experience, such as improving transparency and social responsibility, improving customer support, and reducing the cost of services, falls far short of customer expectations.” Three common barriers to digital transformation in banking There are several common challenges to digital transformation that keep banks from pivoting quickly to meet customer expectations. Roadblock #1: Technical debt As a highly regulated industry, traditional banking relies on complex and siloed legacy technologies that are often expensive to maintain. Over time, technical investments compound, making it increasingly difficult to find time or resources to shift to more modern or scalable platforms. When banks grow through mergers and acquisitions, attempting to integrate additional legacy systems adds to that technical debt. At the same time, banks face increasing competition from the fintech sector — online-first financial institutions that aren’t encumbered by aging platforms. Traditional banks saddled with technical debt may feel that they lack the time or resources to fully integrate, modernize, or replace their legacy technologies. But the longer this debt persists, the harder it is to compete with digital natives, leaving banks less agile in the marketplace. How platform modernization helped make an annuity organization more competitive >> Roadblock #2: Organization size Like many enterprise-level organizations, larger banks often create internal digital teams that combine business, IT, and marketing capabilities and develop expertise in their own technologies, systems, and processes. Faced with competing internal priorities and hampered by regulatory constraints, these internal teams may struggle to get alignment and prioritization for a banking digital transformation strategy and may lack the breadth of expertise necessary to implement a comprehensive modernization effort. Smaller banks, on the other hand, may be more nimble and successful at shifting internal priorities, but they may not have the resources to staff dedicated teams. While organization size is often called out as a hindrance to effective digital transformation in banking, the underlying problem may not actually be a headcount issue. Regardless of size or industry, most companies miss their digital transformation goals due to lack of clarity and strategy. “Digital transformation” in finance or any sector can be hard to define, implement, and measure. A more strategic approach starts with identifying concrete problems or issues, understanding customer needs, and developing solutions that bridge the gap with action steps that are clear, dynamic, and measurable. How technology strategy comes to life >> Roadblock #3: Relying on assumptions about customer needs and wants Understanding customers’ needs, pain points, and experiences can be difficult, and as users adapt to technology their preferences continue to change. This makes audience research even more critical when defining your bank’s digital transformation strategy. After a surge in remote work due to Covid, comfort levels with technology are at an all-time high. Research from McKinsey found that 75% of people using digital channels for the first time during the pandemic indicate that they will continue to use them when things return to “normal.” Not only are customers more comfortable with banking technology, but it has also become an important factor in choosing which bank to use. According to Mobiquity’s 2021 digital banking report, 40% of respondents agreed that they are likely to switch accounts to get better digital tools. Investing in both qualitative and quantitative data can dispel assumptions about your audience, while also revealing specific ways to improve the customer experience. As those opportunities are identified, banks can prioritize technology and services that will have the biggest impact. What goes into a successful customer experience strategy >> How to approach a digital transformation strategy in banking Given these challenges and the continuous evolution of customer expectations, several technologies offer significant potential gains and can help financial institutions stay competitive. Mobile app enhancements Mobile banking apps typically offer the ability to check balances, transfer funds, pay bills, and chat online with a bank representative. By building applications that go beyond these basic services, banks can increase their new customer base while improving customer retention and lifetime value. Leaders in the banking space now include peer-to-peer payments, lending inquiries, and chatbots as part of their applications. However, in addition to monitoring what competitors are doing, it’s important to implement a robust discovery process to see what the target audience wants from a banking app. This could include developing target personas and performing pain point analysis to find unique solutions and services that will better appeal to customers’ needs. From there, financial institutions are better poised to tackle the next layer of technology for the app space — personalization. Many banks are investing in personal financial management tools and customized product offerings in their apps, making banking more accessible and valuable than before. These user-friendly applications and their customization capabilities are an integral part of digital transformation in banking. Refine your mobile applications and provide a better customer experience >> Machine Learning Historically, machine learning engagements have required substantial data science and model training investments. But major ML platforms have evolved, lowering the barrier of entry for these projects. Now, midsize and even smaller banks can use machine learning models to better understand their customers and drive a more personalized experience. And machine learning isn’t just valuable for deepening current relationships; it can also help banks target and acquire new business by identifying trends and opportunities. This means higher quality leads, improved retention, and an increase in business with more potential for high lifetime value. [On-Demand] Reimagining customer insights, risks, & relationships through machine learning >> Data management strategy Traditional lending institutions underwrite loans by using a system of credit reporting. Banks that process loan applications evaluate the risk by looking at credit scores, homeownership status, and debt-to-income ratios. Today, three major credit bureaus provide this information. But these reports can sometimes contain erroneous information, and the information comes at a high cost since it can only be found in three places. And while banks often collect their own internal data, if that data is incomplete or disorganized it cannot offer useful insight. With structured data management strategies, financial institutions can mitigate losses by generating more data and using it to recognize trends and potential liabilities. See how one bank improved ROI by 1054% through strategic data management >> Robotic Process Automation (RPA) Some banking processes are still highly manual. Consider routine tasks like opening an account or reporting a stolen credit card — it takes time to get through the questions, and usually requires a phone call from the customer. With robotic process automation (RPA), in the case of a stolen credit card, the workflow process can automatically cancel the old card, issue a new card, and confirm the mailing address of the new card. RPA can also identify bots or theft with greater accuracy than a human analyst. RPA even has the potential to assist with workload transformation. In addition to streamlining and automating internal processes, RPA can be used to manage the cloud technologies that institutions rely on for their everyday tasks. This leads to more refined workload placement — and therefore a more productive workforce. The bottom line on digital transformation in banking From highly-personalized service offerings to easy-to-use applications, consumer expectations are high in the banking sphere. To keep up with these expectations, banks must position themselves to adapt quickly. Traditional banks are often at a disadvantage to digital-only competitors. Newcomers operate without the burden of legacy systems and outdated business models. But a digital-first attitude can help financial companies effectively implement technologies that enable digital transformation in banking. Find out how one financial services firm successfully handled digital transformation >> Ready to boost your productivity and customer engagement? Let us know your questions and find out how a strategic approach to digital transformation can help your bank thrive in a digital-first world.

The call to create first-party customer data strategies

Mar 9, 2022

Navigating the new data landscape Today’s consumers expect increasingly personalized experiences. To deliver that customized experience and increase market share, business leaders rely on personalization. Ever wondered how ads seem to follow you around the internet? That’s personalization at work. And the engine that drives it is data. The changing data landscape makes it essential for companies to re-evaluate their customer data strategies — and fast. Let’s get started. Where we are now As part of building a direct relationship with your customers, you probably collect some form of data with their consent. This information, known as first-party data, can include purchase history, application and website interactions, opt-ins and subscriptions, and the like. A customer provides a business with first-party data with the understanding that the business —and only that business — will use that data to better serve them. But not all data is collected through active consent. All your customer’s online activity and search history is currently tracked by cookies. That information, known as third-party data, can be sold from one business to another, without the customer’s knowledge, with the goal of getting more information about aggregated pools of consumers with similar behaviors. Think about the last time you shopped for a new car. You probably looked up options online or Googled dealerships nearby. Then, BAM! Suddenly all the ads you saw on Facebook, YouTube, and other websites were from car dealerships, with information targeted to the very model you were thinking about. That’s third-party cookies at work! Using third-party cookies, advertisers pay to get one step closer to consumers’ brains, gaining access to the professional status, consumer preferences, and personal interests that are otherwise outside of their first-party scope. Data in real-life While most organizations collect first-party data from their customers, many still rely heavily on third-party data and use a combination of the two to build their personalized customer experience. For example, using in-store Wi-Fi, Walmart collects a combination of first- and third-party data on close to 145 million Americans. Individual profiles include what customers buy, which stores they visit, and where they linger while they shop. Analyzing every clickable action the customer takes on Walmart.com, social media activity, and even weather data completes a full customer profile. By aggregating and analyzing all this data, Walmart makes inferences about highly personal circumstances, knowing that if you’re buying newborn diapers today, they should start hitting you with advertising for toddler sizes in two years. Even without a loyalty program, Walmart’s data and analytics ensure they know their customers and they have what they need to create a personalized shopping experience. Why these trends matter While customers may enjoy the customization of their experience, they don’t necessarily appreciate the creepiness factor of how often their privacy is invaded. The amount of information that consumers provide — knowingly or unknowingly — on a regular basis, which is then sold to other companies, creates a huge risk of data breaches and identity theft. Many organizations and advertisers see the coming deprecation of third-party cookies as a win for privacy, but a loss for businesses. But it doesn’t have to be a one-sided victory. Smart companies are figuring out how to use first-party data to achieve a personalized customer experience, turning the challenge into a win-win opportunity. Leveraging your first-party data To make up for the loss of third-party data, you need your customers to feel comfortable sharing their data with you. First-party data requires customers to be actively involved. Many customers are willing to give their information if they know it is being used responsibly by the business and that they will receive a better customer experience because of it. Your goal is to communicate how the information customers provide your company enables you to deliver a valuable, relevant experience that fits their needs and leaves them in control of their data. Focus on customer retention over acquisition When you deliver a relevant experience to your customers, they’re more likely to stay customers. First-party data allows you to deliver a more customized experience by gaining an understanding of each customer’s preferences for your products and services. You’re then able to re-engage through your owned channels and increase the lifespan of the customer relationship. And it doesn’t mean you have to leave acquisition behind. Just think … happy, loyal customers are likely to refer friends and family. Offer value in exchange for data Customer loyalty programs are an excellent example of successfully using first-party data, especially where there is a value-add such as accumulating points, getting a discount, early-bird notifications, and the like. In this case, customers are incentivized to provide their data and you can build a unique customer profile that will allow you to customize their interaction with your brand in the future. Create personalized content First-party data helps you discover who your customers are and find out about their habits and preferences directly from the source. Insights gained through first-party data analysis allow you to create different targets and strategies for delivering content personalized to your most loyal customers. It’s a trade-off The reality is, by increasing consumer privacy, businesses lose access to aggregated information and the ability to mass personalize and customers potentially lose the personalized experiences they’ve come to expect. The biggest question remains: can organizations fill the gap and start collecting and leveraging quality first-party data? The time is now Data and marketing teams must work together, and fast, to create first-party customer data strategies ahead of the cookie deprecation timeline. Building processes to collect quality data, keep it safe, analyze it correctly, and use it to create meaningful customer experiences won’t be easy, but laying that groundwork now can position your company for success in the new customer data landscape. This article was originally published on CDO Magazine.

Data executives empowered: The importance of the data community and elevating the CDO

Jul 22, 2021

Almost 20 years ago, Capital One recognized the need for one person to oversee their data security, quality, and privacy, and the role of the Chief Data Officer was born. Now reports show that 68% of organizations have a CDO (Harvard Business Review, 2020). And while the role has become more common and has significantly evolved, many data executives are still struggling to get a seat at the table or bring data to the forefront of their organization. In fact, in a recent survey, only 28% of respondents agreed that the role was successful and established. Company leaders agree that there needs to be a single point of accountability to manage the various dimensions of data inside and outside of the enterprise, including the quality and availability of that data. But now we are at a crossroads — what is the best way to align the work that the CDO does with the strategy of the business as a whole? The reality is that CDOs often struggle to find the internal and external support and resources needed to educate others to align with the organization’s goals. Implementing enterprise data governance, data architecture, data asset development, data science, and advanced analytics capabilities — such as machine learning and video analytics — at scale, is not an easy task. To be successful, data executives need support, resources, and communities focused on the elevation of data. We are proud to continue to help these communities come to life for the benefit of our colleagues and clients, establishing local resources here in the Midwest with global scale, reach, and impact. Read on as Mark Johnson, our Executive Leader for Data Management and Analytics, provides insight on the current state of data and the CDO, and provides details on multiple opportunities for data leaders of different levels to get more involved in the data community. Q: How has the role of data changed/evolved for organizations? The reality is that information is everything. This global pandemic proved that to many organizations. For some, it showed that their digital network was ready, and they were aptly prepared to take on COVID. For others, it has forced them to recognize their own immaturity with data and analytics. On its own, managing data is not exciting — the information just sort of exists. To give data value, you have to put it to use. And so, I think we are going to see the Chief Data Officer and/or Chief Data Analytics Officer really find their own in the coming years. It’s time for their seat at the table. The C-suite is now asking questions that can only be answered with data, and now they truly understand both the value and consequences of the data game. Q: What do you think are the biggest challenges facing CDOs/data leaders today? I think that the biggest challenge for data executives today is the acquisition of talent that is seasoned and experienced where you need them to be for your organization. Higher education hasn’t necessarily kept up with the data world, and often times it takes additional training to reach the right levels. The reality is that right now the talent is manufactured in the real world. Data executives have to be connected and equipped to mentor, train, and keep the right people. Q: You’ve mentioned that data leaders need to connect with each other. What value can people expect from these data communities? I think there is tremendous value. As we are seeing the power of data evolve in organizations, and the role of data leaders evolve as well, I think coming together to collaborate and share elevates the leader, the organization, and the view of data as a whole. In these communities, it gives people a safe space to talk about how they are doing, what they are doing, what their biggest challenges are, and what solutions are working for them. These communities have truly become both a learning laboratory and an accelerator for data. Q: As a big proponent of connecting data leaders, you have been involved in creating different opportunities for people to get together. What groups/events would you recommend, and how can people get involved? I personally have been involved with the MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ), which is such a great opportunity to start with for connection. It has developed into additional opportunities for data leaders at all levels to get involved and create the kind of community we need to truly elevate the value of data. Organizations like the CDO Magazine, the creation of CDO roundtables across the nation, and the International Society of Chief Data Officers (isCDO) all evolved from connecting data leaders and identifying common challenges. MIT CDOIQ: The International MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ) is one of the key events for sharing and exchanging cutting-edge ideas and creating a space for discussion between data executives across industries. While resolving data issues at the Department of Defense, the symposium founder, Dr. Wang, recognized the need to bring data people together. Now in its 15th year, MIT CDOIQ is a premier event designed to advance knowledge, accelerate the adoption of the role of the Chief Data Officer, and change how data is leveraged in organizations across industries and geographies. Fusion has been a sponsor of this symposium for seven years now, and we are so excited to see how the event has grown. Designed for the CDO or top data executive in your organization, this is a space to really connect with other top industry leaders. CDO Roundtables Fusion has always been focused on building community and connecting people. And when one of our clients, a Fortune 500 retailer, mentioned wanting to talk with other data leaders from similar corporations, we realized that there was a big gap here — there was no space that existed where data leaders could informally come together, without sales pitches and vendor influence, and simply talk. That’s how the CDO roundtables were born — a place that allows data leaders to get to know each other, collaborate, accelerate knowledge growth, and problem solve. We just started two years ago in Cincinnati, but now, now we’ve expanded to multiple markets including Indianapolis, Columbus, Cleveland, Chicago, and Miami. These groups are designed for your CDO/CDAO and truly create an environment for unfiltered peer-to-peer discussion that helps solves data leadership challenges across industries. If you’re interested in joining one of these roundtables or starting one in your market, email me or message me on LinkedIn. I’m here and ready to get these roundtables started with executives in as many communities as I can. The more communities we have, the more data leaders and organizations we can serve. International Society of Chief Data Officers (isCDO) Launched out of the MIT CDOIQ symposium, the isCDO is a vendor-neutral organization designed to promote data leadership. I am excited to be a founding member of this organization, along with our Vice President of Strategy, David Levine. Our ultimate goal is to create a space that serves as a peer-advisory resource and enables enterprises to truly realize the value of data-driven decision making. With multiple membership options available, isCDO is the perfect opportunity for data leaders looking to connect with their peers and gain a competitive advantage by focusing on high-quality data and analytics. CDO Magazine I am really proud to be a founder of the CDO magazine, as it really is a resource for all business leaders, not just the CDO. We designed the magazine to be a resource for C-suite leaders — to educate and inform on the value proposition, strategies, and best practices that optimize long-term business value from investments in enterprise data management and analytics capabilities. Check out the publication here. And if you’re interested in contributing content or being interviewed, let me know at mjohnson@fusionalliance.com. Closing: The role of the CDO is integral to organizations, but it’s still evolving. Now more than ever, it is important that data leaders come together to collaborate and problem-solve. Fusion is excited to be a part of each of these initiatives, and we are committed to being an agent of change in the communities we serve and beyond. By connecting global thought leaders we believe that organizations will realize the value of data to power their digital transformation. If you’re interested in joining any of these data communities or just have questions, feel free to reach out to Mark via email or on LinkedIn.

Wearables and mobile machine learning change the face of healthcare

Jul 8, 2021

Advances in mobile health technology have transformed the entire landscape of healthcare, including the ability of physician groups, employers, nursing home facilities, and pharmaceutical companies to capture data in the healthcare space. Gone are the days of paper tracking for your glucose levels and blood pressure. Instead, wearable devices like watches and trackers can seamlessly provide real-time data streams to applications and third parties. The data collected from wearables can be used for clinical research, patient monitoring, and wellness tracking, among other uses. Each data point collected can add complexity to your broader data set. Because of the amount and complexity of data, turning to machine learning (ML) can help organizations leverage their data to identify patterns and make data-driven decisions. By applying machine learning techniques to wearable device data, we can now surface patterns in big data and make predictions about behavior. Machine learning enables healthcare-related industries to leverage wearable device data and identify trends, improve recommendations, and define research outcomes. Popularity of wearable devices Wearables are popular, and their adoption continues to grow. Globally, the wearable technology market is expected to grow from $69 billion in 2020 to $81.5 billion in 2021, an 18.1% increase, according to the latest forecast from Gartner. What’s fueling the growth? Demand for smart devices in the healthcare sector is rising, as is demand for Internet of Things (IoT) devices. Many devices are not fitness-specific, featuring notification of text messages, push notifications for mobile apps, and the ability to pay for items by scanning a QR code with Google Pay or Apple Pay. As such, they have broad appeal. “As a result of the pandemic, we have seen wearable devices become much more than just activity trackers for sports enthusiasts. These devices are now capable of providing accurate measurements of your health vitals in real time. Improved measurement accuracy coupled with the latest advancements in ML make it possible to detect abnormalities before they lead to a major health event.” – Alex Matsukevich, Fusion Alliance Director of Mobile Solutions Types of data collected by wearable devices There are a variety of brands and categories of wearable devices, from mass-market consumer versions to highly specialized types created for niche uses. Apple, Fitbit, Google, Samsung, Garmin, LG, Sony, and Microsoft dominate the market. Though the concept of “wearables” includes a focus on wristwatches, while exercise equipment, glasses, and textile sensors are also becoming more common. Wearable devices can measure: Sleeping patterns Heart rate Irregular heart rhythms Location/route during exercise Pace, stride, and distance while moving Blood oxygen levels Falls Limitations of wearable device accuracy Wearables do have limitations, and accuracy is a concern. Healthcare decisions made using erroneous data could have outcomes detrimental to a patient’s overall health. A study from the University of Michigan reviewed 158 publications examining nine different commercial device brands. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate, and Fitbit tending toward underestimation. But for energy expenditure (calories burned), no brand was deemed accurate. This does not mean that the results are invalid, but that there is a significant difference between results from wearables and clinical results in a lab setting. Wearable devices are constantly upgraded and redesigned as technology improves. And data collected by wearables does not provide a clinical diagnosis. As such, this data is just part of the larger picture of health and can be used only in conjunction with other factors to evaluate your overall wellbeing. Overcoming the biggest challenge of wearable device data analysis Healthcare professionals are already using ML to analyze data for patients. Research published in the International Journal of Research and Analytical Reviews confirms that ML techniques are successful in predicting health conditions such as heart disease, diabetes, breast cancer, and thyroid cancer. The biggest hurdle to incorporating device data into broader data sets is the addition of new inputs, such as hours of sleep or total steps walked per day. Traditional data points such as total cholesterol or blood pressure readings are less frequent, so there is a smaller amount of data overall. The challenge is finding how to best incorporate it into other data sets to create a more comprehensive picture of health. The future of wearable device data and machine learning We can glimpse into the future of wearable device data and machine learning with Microsoft’s recent patent filing. Their potential product aims to provide wellness recommendations based on biometric data, such as blood pressure and heart rate, pertaining to work events. To do this, Microsoft requests access to applications used by employees. Microsoft then tracks data points such as: Duration of time spent writing emails Number of times a user refreshes their inbox Time spent reading emails Number of corrections made when writing emails Recipient list for emails Number of meetings in a day Tone of language in emails By combining this information with biometric data (from a secondary device such as a Fitbit or Apple Watch) and machine learning, Microsoft could begin to understand what work events trigger a response. For example, suppose an employee received an email from their manager. Microsoft might observe that the employee spent a higher-than-average amount of time reading the email and that the employee’s heart rate was also elevated during this time. Based on these insights, Microsoft could propose recommendations for helping employees manage stress levels, highlighting events that trigger anxiety. Patent filing sample image from Microsoft outlining tips and recommendations to improve employee wellness With a broad user base using both Office and Teams already, Microsoft has a deep understanding of work-related events. As Facebook built their business making sense of our social lives, Microsoft has the potential to optimize our work lives. “Wearables combined machine learning will become the new standard in personalized consumer electronics, rapidly increasing in popularity and scale every year until then. An integrated device of the future will be able to get a baseline of your health and will alert you to any abnormalities present. We already see this happening with the new Apple Watch, and it will be very soon that this technology becomes commonplace.” – Michael Vieck, Fusion Alliance Software Developer Wearable devices will transform healthcare experiences Data is the key to predicting, understanding, and improving health outcomes. IBM Research anticipates that the average person will generate more than 1 million gigabytes of health-related data in their lifetime, equivalent to 300 million books. The sheer volume of data means that machine learning will be vital in making sense of it. Paired together, wearable devices and machine learning have the potential to transform healthcare experiences. Today’s applications and uses are only the beginning. Read more: Top 3 reasons to invest in machine learning for mobile Machine learning and wearable devices of the future Wearing Your Intelligence: How to Apply Artificial Intelligence in Wearables and IoT

Using data to drive digital transformation

Jul 6, 2021

While every organization’s journey to digital transformation looks different, one thing remains the same — the importance of data. Tackling your data systems and processes is vital to fully transform. However, the reality is that most organizations are overwhelmed with data about their customers. But these troves of information are completely useless unless companies know that the data they have is accurate and how to analyze it to make the right business decisions. In today’s world, organizations have been forced to pivot and have realized the value data can bring to drive insight and empower their decision-making. However, many organizations have also recognized their data immaturity. So how do you move forward? The role of data in digital transformation Data can be your organization’s biggest asset, but only if it is used correctly. And things have changed. A lot of organizations have completed the first steps in their digital transformation, but now they are stuck — they aren’t getting the results they expected. Why? They haven’t truly leveraged their data. According to Forrester, “Firms make fewer than 50% of their decisions on quantitative information as opposed to gut feelings, experiences, or opinions.” The same survey also showed that while 85% of those respondents wanted to improve their use of data insights, 91% found it challenging to do so. So, now that you’ve got the data, how can you make it more valuable? Data strategy is key to your digital transformation With so many systems and devices connected, the right information and reporting is critical. But first, you have to make sure you have the right technology in place. Utilizing big data Although you might feel inundated with the amount of data you have coming in, using big data analytics can bring significant value to your digital transformation. Through big data analytics, you can get to a granular level and create an unprecedented customer experience. With information about what customers buy, when they buy it, how often they buy it, etc., you can meet their future needs. It enables both digitization and automation to improve efficiency and business processes. Optimizing your legacy systems Legacy systems are critical to your everyday business, but can be slow to change. Why fix what’s not necessarily broken? But just because systems are functioning correctly doesn’t mean they’re functioning at the level you need them to — a level that is conducive to achieving your data and digital transformation goals. This doesn’t have to mean an entire overhaul. You’ve likely invested a lot into your legacy systems. One key to a good data strategy is understanding how to leverage your legacy systems to make them a part of (instead of a roadblock to) your digital transformation. With the enormous scale of data so closely tied to applications, coding and deployment can often make this stage of your digital transformation feel overwhelming. Sometimes DevOps tooling and processes are incompatible with these systems. Therefore, they are unable to benefit from Agile techniques, continuous integration, and delivery tooling. But it doesn’t have to feel impossible — you just need the right plan and the right technology. Focusing on your data quality Even with the right plan and technology, you have to have the right data. Bad data can have huge consequences for an organization and can lead to business decisions made on inaccurate analytics. Ultimately, good data needs to meet five criteria: accuracy, relevancy, completeness, timeliness, and consistency. With these criteria in place, you will be in the right position to use your data to achieve your digital transformation goals. Implementing a data strategy with digital transformation in mind So how do you implement your data strategy? You should start by tackling your data engineering and data analytics. The more you can trust your data, the more possibilities you have. By solving your data quality problem, you can achieve trust in your data analytics. And then, the more data you have on your customers, the more effective you can make your customer experience. But, this all requires a comprehensive data strategy that allows your quality data to be compiled and analyzed so you can use it to create actionable insights. The biggest tools to help here — AI and machine learning. The benefits of a data-driven digital transformation The benefits of investing in your data are clear, including increased speed to market, faster incremental returns, extended capabilities, and easier access and integration of data. Discover more about the different ways you can invest in your data and improve and accelerate ROI for your organization. Ultimately, your goal is to elevate how you deliver value to your customers. Digital transformation is the key to understanding your customers better and providing a personalized customer experience for them. Leveraging your data can make all the difference between you and your competitors. And we’re here to help. Learn more about how some of our clients have benefited from investing in their data and digital transformation.

Digital transformation: Our predictions for 2021 and beyond

Mar 16, 2021

What will the future hold when it comes to digital transformation? We don’t have a Magic-8 ball or special spidey sense, but our team does anticipate sizeable change. We asked a few of our team members what they thought based on their work and personal experiences. Here’s what they’re envisioning for 2021 and beyond. The cloud gains more ground I expect cloud-based business application platforms such as Dynamics 365, Salesforce, ServiceNow, and Workday to drive significant digital transformation within modern workplaces in the next year. Following on the heels of many core infrastructure services moving to the cloud — such as email, servers, files, and data — the next major lift for many organizations will be to modernize and automate their core business processes. I anticipate areas like finance, HR, production, and other critical business operations and workflows will be the next major shift to using cloud-based business application platforms. Moving away from legacy, on-premise solutions is not always a simple task, but in doing so, employees can then work remotely without being tethered to an office environment. Greg Deckler, Vice President, Cloud Services Connect with Greg on LinkedIn Remote workers collaborate differently The work-from-anywhere model has been proven to work and it will continue. However, right now, a Zoom meeting is about the extent of what most people see as remote teamwork — and we all know those can be exhausting. I predict greater adoption of tools like Miro and Mural. These online workspaces allow for active collaborating and co-creating in real time. The need to move quickly and keep pace with digital transformation will require these types of tools, and those who know how to leverage them, to make the most of a remote team’s time together. Doug Scamahorn, Solution Director, UX Design & Innovation Connect with Doug on LinkedIn Cookie compensation I think 2021 will be the year when businesses and marketers confront the pending deprecation of the third-party cookie. Google is driving the industry towards new solutions for retargeting and attribution following the announcement that Chrome will cease to support third-party cookies in 2022. While industry players debate over a long-term replacement, expect to see a scramble to shore up first-party data in the meantime. At a tactical level, this will look like increased pushes for “registered” online experiences where users must explicitly identify themselves, as well as the integrations that power these points of data collection. In the background, businesses will be pushing to connect the dots between online and offline touchpoints using a variety of identifiers, from email to devices to data from “walled gardens” like Amazon, Facebook, and even Walmart and Target. Companies may opt for a CDP (consumer data platform) solution on top of their existing data stack to manage data points specifically for targeted marketing campaigns. When reporting on campaign success and attribution, analysts may need to adopt new tools and strategies for managing “fuzzier” readouts on customer behavior and journey identification. Amy Brown, Solutions Director Connect with Amy on LinkedIn Augmented reality becomes actual reality As mobile processing and bandwidth progresses and matures, we can expect more augmented reality (AR) apps to provide visual assistance in a huge range of applications. I fully expect we will see vehicles with heads-up displays, smart glasses (remember Google Glass?), and other clear displays to be adopted by more companies and thus, individuals. Visual processing in itself is gaining in popularity. Retailers like IKEA are already using AR with their IKEA Place app to enable customers to “see” furniture in their spaces. Microsoft’s recent HoloLens release is a good example of where we’re headed. Jeremy Keiper, Competency Lead Connect with Jeremy on LinkedIn B2B marketers will get more creative There’s always been an understanding that marketing is both an art and a science. Over the last decade, marketers have leaned into the science. Data provided marketers with information about customer behavior that was never available before. Even before the pandemic, B2B marketers were relying heavily on digital channels to engage customers. But pandemic office closures caused marketers to rely on channels like email, webinars, social media, and search engine marketing (SEM), in an attempt to reach prospective buyers who were now working from home. And they had to get creative. Marketers had to be willing to test new ideas and try things that haven’t been “proven,” and to think creatively about how we connect with and engage prospects and customers. I anticipate this to continue, and marketers will use customer data to make sure they understand consumer goals and motivations, then get creative about how to reach out and connect. Kristin Raikes, Sr. Director of Digital Strategy Connect with Kristin on LinkedIn Looking ahead Thinking about the year ahead, we do know that even after offices reopen and things get back to “normal,” the new “normal” will look different than it did before. If people continue to work from home or prefer to engage with brands virtually versus physically, then technology will have to adapt. Are there any major trends not listed above that you think will be a key to digital transformation for this year? If you have questions about specific trends, you can also connect with our team via their LinkedIn profiles above. Our consultants and team members work with clients to improve, streamline, and create actionable change. We create exceptional customer experiences by leveraging data insights, experience design, and technology to transform the way you connect with your customers. Interested in learning more? Let us know, or sign up for our newsletter to get to know us.

Driving digital transformation forward

Mar 1, 2021

Everyone is talking about digital transformation, but there’s a lot of confusion and misinformation about what that term means. Much more than just a buzzword or doing things “digitally,” digital transformation means reimagining your business and driving it forward in a better way. When done correctly, digital transformation can fundamentally change how you deliver value to your customers. That’s what you need to focus on if you want to thrive in this market. Customers today have high expectations and a lot of options, so carefully considering your customer journey is critical to success. Before you get started, here’s an overview of what you need to know about digital transformation and how to continue to drive it forward: What exactly is digital transformation? The evolution of digital transformation Cloud computing fuels digital transformation More than just technology Keys to successful digital transformation The challenges of digital transformation Where are you in your journey? What exactly is digital transformation? Digital transformation is the integration of technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It can often be confused with digitization or digitalization, but they are all different things altogether. Digitization is the process of taking information and converting it from a physical format to a digital one, like typing up paper notes and putting them into Microsoft Word or scanning into a PDF. Digitalization, on the other hand, uses these digital files to take the processes already in place and make them more efficient using the new digital format. Digitalization allows you to make your processes faster but doesn’t evolve the process itself. Digital transformation does more by enabling you to interact with your customers in a new way that is constantly evolving to meet both their needs and your business needs. By ideating and implementing better business processes and technologies, you’ll not only create an elevated customer experience that will result in increased profitability, but you’ll also save significant time and money in operating costs. The evolution of digital transformation Digital transformation might be a popular term today, but this was also true in the late 1990s through the mid-2000s. It started with companies computerizing processes 30 years ago, and as the internet was established, websites started to connect companies with their customers. That’s when Fusion Alliance got started, and we’ve been focused on the end goal of developing solutions for clients ever since. Digital processes emerged to support customer interactions, from sending emails to managing online ordering. As digital ambitions grew, companies realized a need for dedicated digital teams to manage social and mobile channels. Connected to customers, suppliers, and other stakeholders, companies realized the need to connect all of these data silos. Seeing the potential in connectivity, organizations focused on digital platforms connecting all systems. Then they started to experiment with new digital ways of doing business, leveraging data more effectively, and creating greater agility. Today, we live in a world where customer expectations have never been greater. They demand personalized experiences in every interaction with a company’s products and services. Because of this, companies must innovate quickly and deliver. In addition, the pandemic has forced IT leaders to adapt yet again, with many adopting cloud software for video collaboration and building apps that enable workers to enter offices governed by social distance practices and contact tracing. Technologies such as cloud computing, the Internet of Things, and Artificial Intelligence all power the innovation that is able to deliver value. An important element of digital transformation is technology. But often, it’s more about doing away with outdated processes and legacy technology than it is about adopting new tech. It should also about enabling innovation. Cloud computing fuels digital transformation Companies are increasingly moving toward a hybrid cloud infrastructure. From SaaS applications and on-premise solutions to a mix of public and private clouds, hybrid cloud strategies help companies find the right balance for their unique infrastructure needs. Over the past year, cloud providers like AWS, Azure, Google, IBM, and Oracle have made investments to support hybrid strategies. OEMs like HPE, Dell (VMware), and Cisco have also increased investments in tools that enable simpler connectivity between on-premises data centers and the cloud. These investments are all centered on meeting the customer where they are in the moment. Hybrid cloud adoption was already underway before 2020’s pandemic, but the sudden disruption sped things up. Being agile and nimble was, and still is, a significant business advantage. More than just technology Although the focus of digital transformation is often on emerging technologies moving the business forward, true transformation has to encompass much more. Business leaders at the top level must be involved in this change and must be willing to invest in and empower your employees, as well as focus on building your culture. As employees begin to see changes occurring around them, they might begin to wonder how it will affect them and their coworkers. They may question their own position within the organization and try to figure out their own best next steps. During any transition, leadership must communicate with employees to help them feel secure in their own positions as well as the direction of the organization. Head off potential issues by making sure your employees understand what digital transformation is, what it’s not, and how they can be a part of it. Additionally, it may be best to communicate how rapidly things can change throughout this process. Being as transparent as possible and preparing your employees for future changes can make all the difference in ensuring your digital transformation is successful. Keys to successful digital transformation Data from McKinsey shows that when companies achieve transformation success, they are more likely to have digital-savvy leaders in place. Less than one-third of all respondents say their organizations have engaged a chief digital officer (CDO) to support their transformations. Those that do are 1.6 times more likely than others to report a successful digital transformation. Companies that have already invested in data and infrastructure to support technology efforts are more adept at again succeeding. The keys to finding success with digital transformation projects vary because no two companies are the same. However, we do have a few recommendations: Define what digital transformation means to your business Create a map of where your business is now, including people, process, data, and technology. Then define where your business needs to be. The gap identified becomes a roadmap. Defining the gaps are in the business is the first key step in your digital transformation process. Identify and involve the right internal stakeholders Leaders and decision-makers might not have the insider knowledge to execute technical challenges. Your stakeholders should be throughout the organization, across departments, and exist at different leadership levels. Align with a partner to shepherd the process You might find your business well-suited to tackle projects outside your normal scope of work, but many companies lack the internal resources to be able to undertake larger projects. Finding an outside partner might be helpful to keep things moving and provide direction with next steps. The challenges of digital transformation While digital transformation is worth it and necessary to the survival of your organization, there are challenges that come along with reimagining any new business process. To make your transformation truly successful, ensure that you have a good understanding of your brand and your customers. Without that, the entire digital transformation strategy you build will be misguided, and you’ll end up back on square one. Budget can also be a big hindrance for a lot of companies looking to begin their digital transformation. Often, there are additional resources and training needed upfront. Although the cost savings and increased profitability are worth it in the end, the initial expense can be intimidating. Additionally, poor data quality can become a huge challenge for organizations as they go down the digital transformation road. Poor data analytics often leads companies to make important decisions based on misguided data. Poor data also prevents companies from being able to use emerging technologies like artificial intelligence and machine learning, since they prove useless when fed with bad data. Sound strategy and good data will ensure you start your digital transformation journey on the right foot. Where are you in your journey? Whether you’re an established business or a startup, the perfect time for digital transformation is now. No matter how old your business is, your digital transformation will be unique to your organization. It is helpful to think ahead to the future — where do you see your company in the next 5 years? Create a roadmap of where you want to be in terms of customer experience, technology, and data insights, and involve those team members in your planning discussions. By involving decision makers across the organization, you can ensure that you are aligning all parts of your business with the end goal. And, if you can, bring in an experienced third-party to help streamline planning, bring in additional insights, and ensure that your projects run smoothly. Digital transformation allows your organization to deliver the right customer experience to the right people and not only remain relevant in your market, but actually build your brand. One thing is consistent: customers today expect a flawless and customized customer experience, and you’ll need digital transformation to deliver it. Your digital transformation partner Customers today expect a customized customer experience, and you’ll need digital transformation to deliver. When you’re ready, we’re here to help you execute. Need help addressing or assessing where are on your organization’s digital transformation journey? Let us know.

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