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TDWI Upside - Where Data Means Business

Money-Making Analytics (Part 1): Soul Searching and Analytics Strategy Selection

The first step is most important and focused on understanding your company culture, the people you need to involve in an analytics project, and the questions you need to ask provides a framework for conceptualizing and prioritizing your analytics and your project road map.

The buzz around big data and being able to utilize it fully is growing by leaps and bounds. Data professionals are constantly challenged by the size, velocity, and variety of the data available to store, process, and analyze -- but is all data equal? How do you combine the data science with the art of analytics? Storage is cheap but time and effort are not. What data is worth our effort to analyze and process? How does this data get us the right analytics to focus on?

Put aside big or small data for now. Companies need to spend time figuring out the right data and analytics. Analytics that helps companies focus on top-line growth and bottom-line impact will be the winners in the new data-driven world. In layman's terms, go after the juice that's worth the squeeze. Although this seems obvious, what's not so obvious is how to figure out what that juice is or what the optimal squeeze is. In data terms, your company needs to figure out the right analytics to focus on and then choose the right technology and frameworks to generate those insights. You must also determine the best way to inculcate such analytics into the fabric of your company.

This series will review the four essential steps needed for conceptualizing and creating these money-making analytics:

You will also see several examples of such analytics and the steps in multiple industries.

If Data is Science, Analytics is Art!

Does this sound familiar?

  • Why do we have to discuss company strategy and priorities for analytics that matter?
  • Are you sure this analytics strategy should require so much thought?
  • Why do we have to involve these many people in the discussions?
  • Why don't you just give us the reports we are asking for? That's all we really need!
  • Won't all this take ages to do?

These are the most common questions I have heard when approaching company leaders about building data and analytics strategies. Most companies still think of analytics as a set of reports with no thought about the company's driving elements and how each analytics result would fit into the company's decision-making fabric. It should be quite the opposite. Analytics should not be a set of reports but rather a set of action-driven tools that look at history and the current state and provide a predictive view of the future so they encourage decisions and actions.

How do we arrive at the right analytics that requires this kind of thought and design? It's definitely not worth going through the thought and effort of having each set of reports convert into an analytic tool. We need to decide what juice is worth the squeeze. This article, the first in a four-part series, dives into people issues and the questions to ask so you can choose the right analytics for your company. Once we've asked the right questions, we will dive into the framework to assemble, sort, and prioritize the analytics.

This series will review the essential steps needed for conceptualizing and creating money making analytics. from the very beginning -- the soul searching process that companies and data leaders must go through -- and ending in examples of such smart analytics in multiple industries.

Step 1: Strategize with the strategy makers

Begin at the very top and initiate conversations with C-level execs and key members of the board. Be extremely curious about the overall market, competition, the company's edge over others, market threats, and pricing strategies.

Ask questions:

  • What is the overall market size? What's our share?
  • What are the differentiating factors for our products/services vs competitors?
  • What are our biggest challenges in the market now? What will they be in the future?
  • What will make us fail as a company? What will make us the winner?
  • What is our pricing strategy? Are we leaders or followers in the market?
  • What are the top 3-5 focus areas for the company and why? What are we doing in those areas now, and what are our plans for the future?
  • What is the one thing you are missing in terms of tools or analytics? What would having this item be worth?

Step 2: Operationalize with the operational leaders

Once you've spent the time with the strategists, we dive into the next level of detail with the operational and business unit leaders. These are typically the next level down from the C-level staff such as the heads of the major business functions: finance sales, marketing, product management, supply chain, and IT. To dive into operational challenges and opportunities, ask:

  • What are the objectives of our business function in terms of specific growth targets or cuts expected?
  • What are our business function's greatest strengths?
  • What are our biggest operational issues?
  • What do we lack from other business functions in terms of handshakes, collaboration, or tools?
  • Where is the business value leakage? (Business value leakage happens where you see disruption in your business's revenue stream due to operational issues or you see high costs due to operational issue cover-ups.
  • What is the one thing you are missing in terms of tools or analytics? What would having this item be worth?

Step 3: Analyze the nuts and bolts

After spending time with the strategy and operational leaders, it's time to get down to the nuts and bolts with the line managers and the hands-on crew. These are typically the people making the day-to-day calls and decisions about what to ship, how to appease an upset customer, what to put on the marketing collateral, how to sell to this new customer. Often times, I find that these people don't know the details I discovered from Step 1 and Step 2 about the company's strategy or high-level operational challenges. These workers are completely consumed by their individual responsibilities or challenges, making them a valuable resource for learning how the company runs and what makes them make decisions or take action. Be mindful of the individual context, but remember to weave the thoughts with what you heard from the strategists and operational leaders.

Ask them:

  • What is your department responsible for?
  • Whom do you work with in other departments?
  • What are your key decisions? How do you make decisions?
  • What are your biggest roadblocks to getting your job done and what do you see as opportunities in other departments you work with?
  • What is the one thing you are missing in terms of tools or analytics? What would having this item be worth?

After hearing from these key stakeholders and decision makers, begin the careful and detailed work of organizing and correlating everyone's input. I call this the meat-grinding process. It is painstaking and time consuming, but the results are often delicious.

Through this process, use an analytics tool or create a spreadsheet to organize all you've heard so far. You will want to sort, correlate, and prioritize the information you've gathered. This is easier said than done, so make sure you are going through this exercise with someone who's done it before. This is the most important result of this step: a list of the biggest priorities for your money-making analytics. Also, evaluate each item on the whole list; is that item a one-time strategic analysis tool or an ongoing operational tool that will be used over and over again? A tool that provides ongoing value could potentially be worth more, but tools that enable your strategy are typically used once but and are still valuable. Create an analytics tool portfolio that has a good mix of both strategy- and operations-enabling tools.

Next, associate a value number with each of your top-ranked analytics capabilities. In financial terms, the value of an asset is the net present value of the after-tax cash flow of an investment or asset. Each of your analytics capabilities should create ongoing value after the initial build-out investment, otherwise the capability is not worth your effort.

The ongoing value is often calculated in terms of increased revenue, reduced cost, or both. Often this activity will require you to go back to some of your key stakeholders and ask them for the estimate. Calculating the value is not easy, and you can expect resistance. Make this step easier by working with your stakeholders and helping them understand that assigning a value will help alleviate their biggest pain points.

This activity requires considerable people skills, especially persuasion. Also, in most organizations, the business value will have to be vetted by the finance department. In this exercise, prioritize 3-5 analytic domains and areas that are high value and rank them in terms of feasibility and value. This is your final money-making analytics to-do list.

The Three Steps in the Real World

How do those steps work? Here's a good example.

When conducting this exercise for a high-tech company, we discovered that license renewal revenue was considered a major focus area and the biggest revenue driver for the company by the C-level strategists (Step 1). Going to the next level of stakeholders (Step 2), we identified a major revenue-value leak. The company had gone through multiple acquisitions and never unified the systems or data, so when license renewals reps pulled the data about upcoming renewals, they unknowingly passed over major renewals because of data inconsistency (for example, different systems had different customer, partner, or product names across the multiple acquisitions). They left revenue on the table, and business function owners were pointing fingers at each other as to whose problem this was.

Going to the next level -- by analyzing the nuts and bolts (Step 3) -- we identified several issues with the tools, data, and processes. We found a multi-million dollar opportunity. A new analytics tool was created to look across multiple systems and data and allow users to see all available renewals and disparate data. Processes were established to harmonize the data from a more holistic perspective as well.

Looking Ahead

The next article in this series will help you identify the right tools and technologies for enabling the money-making analytics ideas you've identified.

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