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Small Steps For Data Analytics

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Dell EMC

Small Steps For Data Analytics

By Sachin Yadav

In the rapidly evolving IT space, the notion of creating and leveraging data analytics is rapidly gathering momentum, for the all right reasons, including the nature of the complex problems we are trying to solve, the volume of data we need to store and the velocity at which we need to process it to be able to create data models swiftly to answer our complex business questions in real time. For Einstein aptly said, “The solution of the problem cannot be the simpler than the problem itself.”

However, for a multitude of reasons, getting started on this journey remains challenging, where and how to undertake this big effort, being one of the biggest impediments toward its adoption. With the underlying technology, hardware and software, for data analytics reaching a new level of maturity, the strategic framework to formulate the journey is not as much a question of technology, or its underpinning elements, but rather of structural and systemic cross-functional elements that need to be aligned to ensure the journey is effective. On the outset, we will define effectiveness as the ability to achieve the intended business results within anticipated costs and timelines.

My goal here is to outline a conceptual framework that can help organizations and business units to plan and align the relevant stakeholders to maximize the success of data initiatives. I will also highlight the effort we undertook within Dell IT, with our strategically aligned business (SAB), to identify and leverage synergies in the efforts in data analytics during our first SAB Roundtable titled, “Driving Business Value with Data.” SAB members are public companies Pivotal, VMware and SecureWorks—all three of which have Dell as a majority shareholder—and Dell internal businesses Boomi, RSA and Virtustream.

Sir Arthur Conan Doyle, physician and author of the Sherlock Holmes books, once said, “It is a capital mistake to theorize before one has data.” To have data is important, but to be able to identify the relevant data sets and to be able to infer correctly is even more important. Your ability to identify relevant data and its inference is predicated on your ability to identity your key performance indicators (KPIs). Your destination, your business goals, your success criteria and your use cases are the first step toward their realization. To illustrate, if you ever have to ask the captain of the ship its destination, in a word or two, the captain will be able to tell not just the destination but will also be able to map out the journey for you.

In the data center, we are no different. The ability to define our goals from data analytics journey destination, including defining its success succinctly on quantifiable dimensions, is key and should be done with each of the business units that intends to pursue a data analytics solutions. This should help objectively define the attributes of the success matrix and therefore empirically define the gap between current state and future state. Now as you (your business stakeholders) tend to take a closer look on the gap, the emphasis should be on the plausible elements which could be at play and are either constraining your growth or could bolster your time to get you to your end state. Addressing either one separately will help you get closer to your destination, but addressing both simultaneously will help you get to goals fastest.

To be effective, you want to be able to show tangible wins and therefore it is always recommended that you begin with the BU that directly impacts the top line — like the marketing or sales ops team. Within those areas, engaging the correct stakeholders to understand the effectiveness of the current strategic decision making process, including the reliability of supporting data sets, can bring to surface the high value use cases where data analytics can help.

Identifying the right questions to be answered for your business and then creating analytics data models which can answer those questions are the two critical ingredients to the success of your analytics journey and, as you work together, in-turn this process helps to garner trust and alignment from your business stakeholders. Having your IT (analytics team) at the table when you are reviewing the business goals, and the underpinning requirement of capabilities to achieve those goals, will logically take the discussion to the alternate solution states. This will not only reduce your cycle time of “ideas to solution” but, in most cases, can open a new dimension of solution ideas for your business, which can then yield the next set of iterative possibilities.

To summarize, informed decision making through intelligence and cross-functional (business – IT) alignment can trigger a virtuous cycle of data-driven plans and results. The cycle, which starts with clear illustrations of goals and use cases, moves toward identifying strategic questions to be answered. KPI(s) are identified, and finally the solution implemented yields effective data modeling along with integrated business planning. This effort guides the effective execution of business plans and standardization of processes while creating agility in operations, and results in higher predictability. As program results are realized, ongoing evaluations help with recalibration of the course and plans, which is tightly coupled with data sets and modeling. This essentially creates the virtuous cycle of data-driven results generating ever-increasing value add in the execution of business processes.

In my next blog, I will delve deeper into the recalibration and commitment to the cycle, as well as how we brought together strategically aligned businesses within Dell Technologies to share their current goals, capabilities, challenges and roadmaps in order to determine how data is and can be converted into insights to fuel business growth.

Sachin Yadav is the senior manager, IT business consulting at Dell IT.