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The Five Building Blocks to High Confidence Decision Making in the Digital Age

In this special guest feature, Naresh Agarwal, Head of Data & Analytics at Brillio, outlines useful building blocks to understand how to analyze the right data to make impactful action, including: the use of technology and skill sets; asking the right questions; building a culture and mindset that encourages the use of insights to promote high confidence decision-making. Naresh is a business leader with proven experience in delivering transformation programs and growing services portfolios, strengthening client relationships and managing and influencing global delivery models. At Brillio, he oversees the Data & Analytics team, which enables customer-facing and operational insights with the power of big data analytics. Brillio is a business solutions and IT consulting company that creates innovative digital experiences for companies in banking and finance, utilities, CPG, retail, and more, to engage, surprise and delight their customers. Naresh oversees the development of data-driven strategies that are key to market differentiation for customers and help them become enterprise-grade analytics organizations.

Today’s Digital Age has brought a dramatic shift in customer expectations. Consumers now have numerous options in terms of where they shop, the products and services that are available, and with whom they do business. Savvy shoppers are also adept at using the technology at their fingertips to “webroom” and “showroom” so they can get what they need, when they need it, at the price they want. As a result, we’re seeing a proliferation of brands transforming into digital first businesses in order to stay competitive and relevant.

This rapid change – and the digital first transformation – means companies have less time to react to market shifts and make strategic decisions. In this new market environment, businesses must quickly evaluate the impact of activities and act intelligently and decisively. Big data is the backbone of this major shift. Yet big data – or more specifically, the data explosion that comes from businesses having insights into what customers are doing across multiple touch points – is both a blessing in terms of the opportunities it provides, and a curse. Such massive amounts of data can be overwhelming for both IT and various business units, and making sense of what is important and what is not can be a huge challenge.

In order to enable smart change, it’s not enough for companies to understand what consumers are doing. More importantly than the “what” of customer actions, companies must understand the “why” behind customer choices and behaviors, and the question of “what next?” must also be asked in order to plan for the future. There are five key building blocks that companies must put in place to make sense of the big data explosion and reach high confidence decision-making. So let’s dive right in.

1. Build the Foundation: Trust the Data

When embarking on any project, the foundation must be solid. In the case of big data analytics, this means establishing trust in data. All the bits and bytes and metadata that are leveraged need to be clean and trustworthy so that decision makers have confidence in knowing the insights gleaned are the right ones. In addition to data quality, it’s critical to look at the vast array of data sources holistically as an interconnected system of processes, and enable decision makers to look at the lineage of the data to inherently trust that data. Because data is not still or stagnant, building a data pipeline that is clean and also shows the data lineage is critical to establishing trust in data.

2. Build the Team: Incorporate Strong Learnability and Collaboration Skills

Big data analytics unquestionably revolves around technology. But creating meaningful data analytics processes means that stakeholders beyond a company’s IT organization and data analysts should be involved. Big data analytics is a team sport and requires people who can work together as a team. The most actionable results become clear when companies bring in people with varied expertise, including data engineers, data analysts and data scientists, as well as business experts with domain knowledge and a keen understanding of business processes. Another important aspect of this process is each individual’s ‘learnability.’ Because big data analytics is rapidly evolving in terms of the tools, processes and technology, what was relevant a few years ago has been completely transformed. With a team of people who learn new things quickly and collaborate effectively, the team can work with the data in a way that is unique to the individual company and its industry, ensuring the insights generated are relevant and actionable.

3. Build the Framework: Discover the Unknown Questions

The need to maintain market relevance – and impact both the top and bottom line – means companies need to ask deeper, more meaningful questions of their data. Having the right question or problem statement framed correctly is half the battle, and the usual known business performance questions (e.g. what are my sales? is my marketing budget on track?) are no longer enough. The value that analytics brings is the ability to identify unknown questions to ask, and to answer those questions. In order to deliver impactful change, deeper questions must be analyzed, such as how sales performed last month, why they were down, and how to reverse the decline. A deep dive into big data analytics gives companies a stronger understanding of factors like customer preferences, behaviors, and choices, and employing predictive and prescriptive models allows organizations to test various scenarios and chart the right path forward.

4. Build for Long-term Success: Collaborative Ecosystem

As stated above, technology is the bedrock of big data analytics. Success in this endeavor is accelerated when the company and its ecosystem of partners have an ingrained partnership mentality. This ecosystem includes technology partners and service providers, as well as the infrastructure provider, platform provider, customers and more. Collaboration among all of these partners is required for long-term success. The partnership approach ensures that data is not used in isolation by any entity, and a high-touch engagement model that focuses on creating a learning environment allows all parties to see the data and business problem as an interconnected system. Successful partnerships enable any service or technology provider to contribute more than just their own piece; the provider’s team members should act as an extension of the client’s team, question assumptions, bounce ideas off each other, and test and validate hypotheses. A true collaborative approach to big data analytics contributes to the credibility and validity of the data and helps to ensure that the insights generated will be trusted and acted upon.

5. Build the Environment: Culture and Mindset

The four building blocks outlined above will be met with failure if organizations don’t create a culture of open-mindedness and nimble action, where failure is not only accepted but encouraged. This means eliminating established, entrenched ways of doing things, and understanding that when team members are trying to answer unknown questions, they will not always be successful. This approach where a fear of failure is removed from the equation requires buy-in from senior executives in both words and action, and key stakeholders must step up as champions of the big data efforts. In addition, companies must see data as an asset similar to labor and capital, and be ready to make the investment needed to acquire and build data. Organizations must empower employees to use big data tools and the resulting findings by implementing organization-wide processes that put analytics tools in employees’ hands, drive adoption of big data methodologies, give employees the ability to use and act on information, and embed instilling insights into decision making processes.

Swiftly and constantly changing market demands, increased competition and an accelerated, exploding volume of data have become the new norm. While there is no silver bullet companies can rely on to solve all of these challenges, big data analytics plays a key role in helping organizations become more agile – from an IT, customer responsiveness and market leadership perspective. If properly adopted and executed, big data analytics can generate valuable insights that enable organizations to become nimble, anticipate unknowns, and make important business decisions with a high degree of confidence to maintain a competitive edge.

 

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