>>How is your organizationinvolved with big data?

Andrew Appel: IRI is a leading provider of big data, predictive analytics and forward-looking insights that help CPG, OTC health care organizations, retailers and media companies to grow their businesses. We’re one of the original innovators of big data, and it remains the core of our business today. Our industry-leading technology platform, IRI Liquid Data, and AI/machine learning tools help our clients manage a universe of data points and mine it for critical insights.

David Fussichen: Since our founding, Analytics8 has been helping clients transform data

into knowledge. We help clients prioritize their needs and get them on the right path quickly—and that means different things for different clients. Most of our clients have various legacy databases and a desire to move to a modern data architecture. Our consultants work with each client to provide guidance on how to best use their data to solve their unique business problems.

Jill Huletz: We use big data every day, as a way to make us more efficient as well as provide insight into better serving our millions of customers. This requires us to consider how we collect, secure, store, transport, and transform the data for use. For each of these critical

components we must consider the right approach and balance the desire to leverage every data element in the context of each element. Using the data to drive efficiency and insights requires a strong foundation of these upstream processes.

George Asante: Pareto Intelligence is a leading healthcare analytics and technology company modernizing the way health plans and providers succeed in value-based care. We deliver market-leading solutions that unlock insights from big data to support improved performance and more informed strategic decisions. Underpinning all of our solutions is our data management platform, which

ingests, normalizes and enriches various healthcare big data sources—including claims, clinical and socioeconomic details (determinants of health)—to enable efficient downstream analytics and applications. The platform is built for both batch and streaming data ingestion, which is critical to organizing big data at scale.

>>How can big data andbetter analytics help abusiness succeed?

Huletz: Better analysis can position a company to compete and drive smarter, more efficient business strategies. Algorithms can search a huge amount of data to find subtle patterns to help predict consumer behavior more effectively—which can lead to better decisions to drive revenue and customer loyalty. In addition, these new technologies can automate simple processes to make business more efficient while also reducing risk.

Fussichen: We preach to our consultants and clients, “organizations that effectively use their data assets will ‘win’ and those that don’t will lose to their competition.” The stakes are incredibly high. Through better use of data, we’ve seen positive transformation across so many industries. For example, retailers improve relationships with franchises, manufacturers improve product quality and reduce worker injuries, asset management companies increase returns, insurance companies reduce risk for their clients, and even fire departments save more lives.

Appel: Strong data and analytics capabilities are some of the last remaining sources of competitive advantage, particularly in the CPG and retail industries. In today’s rapidly evolving retail environment, e-commerce has lowered thebarrier to entry, big companies

are losing market share to disruptive new entrants, and shoppers are less brand loyal. As a result, a relentless focus on consumer needs and speed to meet those needs is critical to driving growth. Access to the big data, and the sophisticated tools needed to manipulate that data, gives our clients access to fast, accurate and actionable insights on how to improve their business.

Asante: To remain competitive in today’s business world, it’s imperative that every business decision is underpinned by data. By collecting data on your user experience and how customers use your product, and then making strategic decisions based on that data, you create solutions that become critical to end users. This alone can give you a competitive advantage, or if you’re an established organization, it will help stave off disruptors.

>>How has big data analysissolved a problem for yourorganization or one of yourclients?

Asante: Using the data management platform mentioned earlier, our analytics have identified over $1.5 billion in financial improvement opportunities for our clients. Because of this success, we’ve commercialized this capability into our Healthcare Data Integration solution, which allows clients to collect and organize healthcare data in all formats and types quickly and efficiently, decreasing data latency and increasing speed to insight.

Huletz: One way we use big data is to evaluate customers’ transactions to provide them with relevant offers and messages. For example, by evaluating how a customer is navigating our website and tracking the pages they’re viewing, we can personalize their initial landing page

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>>> As the volume of data that businesses try to collect, manage and analyze continues to explode, spending for big data management and business analytics technologies is expected to reach $260 billion by 2022.

The growth is fueling a continuous stream of big data technology start-ups, while also pushing more established players to deliver new and updated products. It’s been said that a big data focus is required for practically any company to compete and thrive today.

Crain’s Content Studio gathered insights from local tech executives on this data revolution.

CrainsChicagoBusinessAd19_6x6_Final_071119.indd 1 7/11/19 9:03 AM

TECHNOLOGY: BIG DATA

A ROUNDTABLE DISCUSSION

to provide more relevant information and offers.

Fussichen: Crow Wing County, Minnesota, wanted to reduce criminal recidivism, or the tendency for a convicted criminal to re-offend. By analyzing their data sources and applying various analytics techniques, they can now easily gather all the information needed to create customized, informed discharge plans. As a result, they’re equipped to reduce crime, create better lives for ex-cons and improve allocation of county funds.

Appel: A beer manufacturer was looking to optimize the assortment of brands and pack sizes it stocks across 200,000 stores nationwide. We used over 20 data sets to capture the beer manufacturer’s sales and shipments, local preferences in consumer behavior and substitution effects. We designed a solution that leveraged our data, algorithms, AI capabilities and unique platform to deliver specific, prescriptive actions to enhance profitability. While the beer category overall has experienced declines, our client has increased its profitability by implementing our suggestions.

>>What are some best practices for an organization wanting to get started with a data strategy?

Fussichen: A good strategy starts with clear and concise goals, including the key challenges to overcome and the business-critical questions that need answering. Then, determine what data is needed to accomplish those goals and how to source the data. Next, define infrastructure requirements, such as data integration, data storage, privacy and security—not only the current requirements but also future needs. Then, choose a toolset that will help turn data into insight, and equip employees with the right skills

and processes that encourage data-driven behaviors. A documented roadmap is critical to serve as a playbook for how and when everything will be accomplished, including technical details and an overall timeline.

Huletz: Before you jump to the how, it’s important to understand what makes up the data analytics ecosystem. The better your data, the easier everything else gets. Don’t invest in the latest and greatest technology before identifying how you’ll use the technology. Test different variables and biases; otherwise, effects can be impossible to disentangle. Finally, eliminate the silos and work on talent integration; you’ll need to combine business insights and technical skills for a solid data analytics strategy.

Asante: It’s tempting to believe that establishing analytics competencies and building data models will expose amazing insights. But it takes much more than that. Data strategy is transformational, and organizations must think of data as the engine that will power their businesses. To truly succeed, you must be intentional about the insights you pursue. Start by identifying key business objectives, and then identifying the data assets needed to achieve those objectives. In the end, you want capabilities and competencies that enable your organization to intuitively connect people, processes, data and outcomes.

Appel: The most important first step is planning ahead. As an organization scales, the amount of data to be processed will increase exponentially, and big data needs more processing power than you would think. Secondly, it’s important to understand how your data will feed actionable

insights and structure your dataset accordingly. We also recommend that organizations observe and learn from other businesses that have a successful data strategy.

>>What are some typical challenges organizations face around big data?

Huletz: Getting the right focus on the data itself and being very clear on the problem they’re trying to solve. It can be tempting to rush to implement a solution, without awareness of the data required to provide the necessary insight.

Asante: Mismatched priorities are a common challenge, meaning that people who

understand the technology don’t always understand the business impact, and vice versa. That’s why a cross-functional effort to coalesce ideas and identify use cases is so important.

Fussichen: One of the biggest challenges is deciding who’s responsible for an organization’s data assets. Most large organizations have a centralized IT-sanctioned process to access and use data along with multiple decentralized pockets of data and analytics efforts. Many are trying to gain control of their organization’s data and analytics assets while still providing flexibility and easy access to those that need it. It all starts with asking, “Who’s responsible?”

Appel: One common challenge is feeling simply overwhelmed by the amount of data that exists, so that companies don’t know where to begin their analyses. Another common problem is with integration. Retailers frequently receive raw data from a variety of sources, which can lead to headaches when trying to align the different formats that the data is delivered in.

>>What are your thoughts on outsourcing the solution?

Appel: Having a thorough understanding of the scale and use of datasets will largely inform the decision. When working with different types of data coming from

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Activating healthcare data to improve outcomesPareto Intelligence is a leading healthcare technology company modernizing the way health plans and providers succeed in value-based care. We provide analytics and advisory solutions to help our clients improve performance and make sense of complex healthcare data to inform strategic decisions.

paretointel.com

Ingesting, normalizing and enriching various healthcare data sources to enable analysis

Ensuring complete, accurate and compliant data collection and submission to optimize revenue capture

Deploying proprietary analytics and data modeling to improve performance

Distributing insights to providers within workflows to improve patient outcomes

ANDREW APPEL President, CEOIRI>>andrew.appel@iriworldwide.com>> 312-726-1222

GEORGE ASANTEChief Technology OfficerPareto Intelligence>>gasante@paretointel.com>>312-476-8929

DAVID FUSSICHENPresidentAnalytics8>> dfussichen@analytics8.com>>312-878-6600

JILL HULETZProduct Analytics ManagerBank of America >>jill.huletz@bankofamerica.com>>312-992-0747

both internal and external sources, outsourcing can be beneficial and will save time and frustration. However, one thing to keep in mind when considering outsourcing data management is that many third-party platforms offer a variety of products that can be confusing with seemingly overlapping capabilities. Having a complete understanding of what analyses you need and the capabilities of the outside platforms is essential to ensure you are not paying for solutions that you will not use.

Huletz: Figure out the problem you’re trying to solve, then determine if it makes sense to leverage external resources. There are plenty of cases where buying capabilities can make sense, such as cloud computing or storage. Outsourcing may seem like the low-investment, low-risk option, but make sure you’re thinking longer-term. Without a fully baked strategy, relying on external vendors could circumvent your digital transformation. No external partner will understand your business better than you. Tapping into the power of your

data relies on understanding your unique business strategy, problems and opportunities. As difficult as it may be, transforming your business model and strategy is one of the single best ways to future-proof your business.

Asante: Generally, if you need big data analytics, but it’s not a core part of your business, it makes sense to outsource. Or if there’s an immediate need for a specific capability, it’s worth looking to a specialized vendor as a short-term solution. For many organizations, big data capabilities are becoming so critical that rather than strictly outsourcing, it’s more beneficial to build these competencies within their four walls, particularly if they partner with an organization with proven success helping other companies develop and implement a big data strategy and infrastructure.

Fussichen: Outsourcing is probably the number one thing an organization can do to start achieving success with their data. Working with someone who’s “been there and done

that” can save millions of dollars and months or years of effort. Offshoring—in an attempt to save on labor costs—is one of the worst things an organization can do, at least when it comes to strategy or project work. Working directly with clients, face-to-face, to develop, design and implement data and analytics solutions is far more efficient and effective. However, offshoring can be a good way to handle support and maintenance efforts.

>>With big data comes big responsibility. How can organizations manage compliance and security?

Appel: Encrypt data in all mediums and tightly restrict access to a small group of educated and trained professionals. We’ve developed policies and notices, along with our internal policies, procedures, and training programs, to ensure that to the extent we collect, use and share personal information in the course of our business activities we do so in accordance with the highest data protection and privacy standards.

Asante: Before you decide to use a technology, put it through a robust vetting process to ensure your data doesn’t end up in the wrong hands. Build compliance and security competencies internally, so that when you onboard new technologies, the focus is not only on the implementation of the technology itself, but also the security around it.

Huletz: Data security is not—nor should it be—the sole responsibility of the IT department; more and more CFOs and CEOs are taking ownership of data security as a key business strategy. A few must-haves include being transparent with what you’re doing with data, obtaining customer approval and knowledge—for instance, having them “opt in” instead of “opt out”—building security firewalls, and designing a scalable, flexible security system to adhere to evolving global regulations.

Fussichen: Our consultants follow several safeguards when they work with clients’ data, including third-party security audits, encrypted everything, a no-thumb-drive rule and anti-phishing policies. We also recommend that clients make data and network security one of the top priorities of the CIO, who should be given the authority and resources to implement the right systems, tools and processes, and to hire the right people.

>>Technology innovation is outpacing business evolution. How are companies handling the change?

Asante: Companies generally fall into one of two categories—technology native, or founded on technology, and technology immigrant, with separate business and technology functions. For technology immigrant companies to

advance into technology native, they must bridge the gap between business and technology by building more collaboration between the two and using technology as an enabler for business, rather than a separate function with a specific purpose. Technology leaders must have a seat at the table from the beginning, so that as business strategies are discussed, any efficiencies can be identified and concerns can be eliminated during strategizing.

Appel: Most large, established businesses are resistant to wide-scale change, and are cautious when adopting new technologies until there’s more broad acceptance of that technology. The Internet of Things is a good example of this; while the technology to achieve this type of communication has existed for years, we’re just now starting to see it be commercialized and implemented on a wide scale.

Huletz: The biggest companies, including the fintechs, have the funds to invest and they’re driving a lot of innovation. The successful ones are laser-focused on their competitive leverage points and applying technology to do those specific things well. Middle-market companies are in the toughest spot to drive innovation and keep up.

Fussichen: It’s easy to get dazzled by cool tech and a good sales pitch. In fact, big data as a concept has already led companies down dead-end paths. Companies that stay focused on supporting business objectives with the correct technology will thrive and survive. Those that chase new tech for tech’s sake will not.

>>How will new analysis techniques like AI and machine learning impact the use of big data?

ANDREW APPEL is president and CEO of IRI, a Chicago-based provider of big data, predictive analytics and forward-looking insights that help CPG, OTC health care organizations, retailers, financial services and media companies grow their businesses. Previously, he held executive roles with Accretive Health, Aon, and McKinsey and Co. He holds a bachelor’s degree from UCLA and an MBA from the University of Chicago, where he was the Henry Ford II scholar.

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TECHNOLOGY:BIG DATA

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JILL HULETZ leads the secured product analytics team that supports Bank of America’s Consumer and Wealth Management businesses by delivering insights to drive short-term business results. She has over 30 years of consumer financial services industry experience, including previous roles in business leadership, analytics, project management and information technology at BMO Harris, PNC Bank and Stewart Title. She holds a bachelor’s degree from the University of Wisconsin - Madison.

DAVID FUSSICHEN is president of Analytics8, a data and analytics consultancy with 10 offices in the United States, Australia and United Kingdom. He started Analytics8 in the U.S. in 2005, leading it for the last 14 years. Previously, he spent more than 20 years in consulting with Business Objects (later acquired by SAP) and MicroStrategy. He holds a bachelor’s degree in industrial engineering from Ohio State.

GEORGE ASANTE is chief technology officer of Pareto Intelligence, a Chicago-based firm that delivers artificial intelligence-enabled analytics and technology solutions to more than 40 healthcare clients nationwide. He has nearly 20 years of experience in information technology, spanning software development, enterprise data management and architecture. He holds a bachelor’s degree in computer information systems from Baker University and a master’s in medical informatics from Northwestern University.

Fussichen: With accessible machine learning, companies can augment their human analysts with machines and actually make use of that vast amount of data sitting in a data lake. We’ve seen clients utilize machine learning to shift from traditional analytics—looking at why something happened—to more predictive analytics, taking control of a situation before it happens.

Asante: The highest level of data-driven practices is autonomous decision-making, where the “machine” makes decisions and serves them to humans to evaluate. AI and machine learning also help highlight patterns, identify anomalies, define trends and more. Because of the sheer volume of big data, we need analysis techniques like AI and machine learning to unlock the insights within the data.

Appel: Embracing these tools will transform the role of research professionals from generating insights “on demand” for the millions of decision-makers in their firms, to enabling, teaching and implementing tools that improve the speed, depth and accuracy of strategic and tactical business decisions. This disruptive shift will build the tools, algorithms and platforms necessary for the salesperson, brand manager, store owner and media buyer and others to make the optimal, data-informed decision they need to drive their growth.

Huletz: These solutions will continue to drive the use of big data. They’ll become easier to use, but will require even more focus on the input, the data and the interpretation of the output.

>>Can certain industries benefit from big data more than others?

Fussichen: No matter what industry a company is in, they

have an opportunity to use their data to get ahead of competitors and offer better customer service. I see that financial services companies seem to use their data in new and innovative ways. We’re also seeing big improvements in the way companies handle distribution and logistics data.

Appel: CPG brands and retailers require a complicated combination of logistics across departments, a deep understanding of changing consumer preferences, and a focus on innovation to maximize their returns. This means they have many different levers at their disposal that can be pulled to make sure their processes are optimized. The granularity of today’s datasets compliments the optionality of the CPG/retail environment more than other industries.

Asante: Healthcare is one industry worth noting because of the volume of information, interactions and touchpoints—what we call data friction—along the healthcare continuum. Having access to the right data about an individual—not just their health status, but also activities of daily living, like sleep quality, exercise, food, etc.—can help doctors better care for people to help them get and stay healthy. However, the more data you collect about a person, the more privacy becomes a concern. For those working with big data in the healthcare industry, compliance and security must be the highest priority.

>>What else would you like to share on this topic?

Asante: Business leaders should not shy away from big data because it’s intimidating, or push it off as an IT directive. While IT plays a major part in developing and implementing a big data strategy, the business leaders should be the ones driving the technology. Every

company should seek to become technology native. It’s also important to have a culture of experimentation, where failure is accepted as part of doing business. Every organization is unique and will need a different set of big data capabilities and technologies to advance the business. Some things will work, some won’t. Fail fast. Learn from it. And move on. If you don’t have a culture of experimentation, where failure is accepted as part of doing business, it becomes very hard to onboard big data technologies.

Appel: One often overlooked stakeholder group that benefits from the usage of big data is

consumers. Using data, brands and retailers can ensure that they’re stocking the products that consumers enjoy most, innovating in the most desirable areas and creating ads that consumers find relevant and helpful. An investment in data analysis and actionable insights drives growth for our clients, and enhances the experiences of their customers.

Fussichen: Having a trusted data and analytics partner is key to technical implementations, but also to staying well-informed of the big data landscape. Let the vendors show you their stuff, but find someone that provides guidance and can help you cut

through the noise to get on the right path quickly.

Huletz: The data revolution is also affecting hiring. Acquiring specialized tech talent is a tough proposition, given today’s low unemployment rate and strong economy. While some believe technology will eliminate jobs, others argue it will create new jobs for skilled workers. The transition could be as dramatic as the changes experienced in the industrial age, when production lines and automation techniques radically enhanced productivity in manufacturing.

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