In my last blog post (The Data Map—The Road to Managing Data as an Asset), I provided advice on how an organization can go about building a data map in order to establish a baseline of the data assets underlying their enterprise. Now, before we look at organizing those assets for analytic purposes, let’s talk about a different challenge that is foundational to everything else—becoming data driven. You probably hear this phrase a lot but may not necessarily see a lot of useful discussion about what it means, in practical terms. 

Let me start with my own personal core belief about this. Becoming data driven has a lot LESS to do with software, tools, BI, analytic techniques and wonder weapons, and a lot MORE to do with your organization’s culture, leadership, and philosophy about decision making and decision support.

Things that do not make your company data driven:

  • Buying an expensive business intelligence tool and the necessary usurious licenses and installing it on a multitude of desktops.
  • Hiring data scientists and creating CDO titles.
  • Creating a data mart, warehouse, or lake and accumulating metric tons of data.

Most of these things may have a place in a company’s evolution toward being data driven, and they may be necessary (at some point), but they are definitively not sufficient (at any point) to drive the transformation. Put another way, in my years as an analytics consultant, I have worked with many smaller companies whose toolkits were basically just Excel based and were far, far more data driven than giant enterprises that had spent millions on the items illustrated above and were failing to address the core changes necessary to actually utilize them.

Being data driven means having what fighter pilots call maximum situational awareness— striving for near perfect clarity on the state of your operation, and relentlessly seeking highly informed insight into what is likely to come in the near, intermediate, and longer-term future.

In the absence of leadership making the difficult changes to their operational processes, companies don’t fully utilize the capabilities these toolkits deliver in a data driven manner. You fundamentally have to a) trust the data and b) be willing to have the courage of your convictions to drive the outcomes the analytics illuminate. Those convictions are often torpedoed by leadership-centric issues of politics, expediency, procrastination, or cults of personality. I have worked for companies where that list constitutes the entire operational methodology. We laugh, but everybody reading this knows it’s true, and probably sees some of it at their own company every day. If you’re a leader, and your reaction is “not at my company,” well, good luck!

Let’s not kid ourselves. These are very common problems, to a greater or lesser degree, at many companies. The kinds of organizational behaviors and dysfunctions are the biggest barrier to becoming data driven, not the lack of shiny tools and cool software.

So, this begs the question—what is the CEO, CMO or COO who is truly committed to making this happen supposed to do?

At its very core, becoming data driven means being fact-driven. It means making more efficient, informed decisions. It means having what fighter pilots call maximum situational awareness—striving for near perfect clarity on the state of your operation, and relentlessly seeking highly informed insight into what is likely to come in the near, intermediate, and longer-term future. It means embracing measurement and celebrating the results—both good and bad. Those qualifiers, by the way, are probably holding you back right now. What you want, as a manager, is accuracy—and if you want to get your people in the habit of thinking that way, you should be substituting “accurate” and “inaccurate” as your key descriptors for your numbers. Don’t punish people, at all costs, for bringing you numbers or analysis that you don’t like—if it is accurate. Reward honesty in measurement, regardless of the relative interpretation.

If you are committed to reaching this goal of becoming data driven, then you are likely going to take your company on a journey through the three levels of analytics:

Descriptive Analytics: Focused on maximizing the utility of the datasets generated by your current operation, supplemented with other data sources, to maximize efficiency.

Predictive Analytics: Deploying tools that will allow your operation to anticipate customer needs and to model forecasts and scenarios of possible business scenarios (product launches, for example).

Prescriptive Analytics: Currently much debated in definition, but grounded in the implementation of advanced AI and machine learning techniques to address complex, multi-variate questions. Characterized by a state of maximum automation, it can be thought of as the point where smart machines begin to manage much of the operational decision making in an enterprise.

So, how to begin that journey?

Issue an RFP for an advanced BI tool, right? WRONG! You have homework to do, my friend. Developing the roadmap that will eventually guide you through this journey means coming back to the core of what being data driven means—understanding the current state of your operation, and what the priorities are for you to achieve.

  1. Drive revenue and growth
  2. Reduce expense and grow margin
  3. Increase the efficiency of the operation (in many cases, cost avoidance rather than cost reduction)

To make these things happen, in a data-driven way, look at what barriers are blocking progress across these strategic goals. I advise a company to start with a very straightforward exercise—the Business Threat Assessment.

Becoming Data Driven: Step 1—The Business Threat Assessment

This is the foundational step to all that follows. It establishes the priorities that analytic solutions need to address, and it’s entirely in the control of the company to achieve. The company needs to answer three fundamental questions:

  1. What are the three greatest tactical (next 1 to 2-year horizon) threats to the operation’s success?
  2. What are the three greatest strategic (next 3 to 5-year horizon) threats to the operation?
  3. What are the three greatest, persistent operational issues the company seems to face, year after year?

Some words of advice about this: If, upon reading this list, your first impulse is to reach for the phone and call a highpowered (expensive) business consultant to come in and execute this, you’re already off the rails. This is an exercise that any company should be able to accomplish without any external help—and if you can’t, you have bigger problems than analytics can fix. Get the bright leaders in your company to spend a day on this. And, if your feeling is “I can’t trust this to be done right,” then do pick up the phone, call a recruiter and get on top of your real issue.

Let’s be honest. Anybody in a leadership position should have a pretty good idea of the answer to these questions. If you’re not talking about them today, in a regular fashion, then your first step on the way to becoming data driven is to institutionalize this list, refresh it on a monthly basis, and focus the leadership team on addressing it.

Why is this first step necessary?

Because being data driven means committing to a process of continuous improvement. As leaders and managers, you are prioritizing your people, their assets, and their efforts. If you’re not clear on the size, pressure and importance of the challenges facing the enterprise, you’re not able to task anybody effectively. You should be developing plans that deliver the maximum return to the business along the three key metrics we’ve discussed (growth/cost/efficiency), and those plans are NOT one and done. They are a continuous, reinforced, optimized set of decisions that are consciously selected to deliver maximum, measurable return.


In our next post, we’ll talk about how to take the outputs of the BTA, and do the exercise of asking the question, “How can my current data assets and KPIs help me address these challenges?” We’ll be in the land of descriptive analytics and talk about taking a hard look at your current reporting infrastructure before you spend a dollar to change it.


(Article by Chris Schultz, a principal at Analytic Marketing Innovations (AMI) and a RUMBLE Strategic Partner. Their solutions delivery approach identifies executable steps and recommends both near-term and long-term courses of actions, helping your business leverage data insights for growth and transformation.)

You won’t be surprised to learn that here at RUMBLE, we believe your data is an asset to your enterprise – just like your people, your innovation, your plant, and your capital. Smart business managers are in the business of maximizing the utility of their assets. They make sure that people, money, and equipment aren’t left unnecessarily idle, and they are constantly seeking ways to leverage those assets to achieve the fundamental strategic goals every business has to serve if it expects to prosper:

  1. Drive Revenue
  2. Decrease Expense
  3. Increase Productivity (Efficiency)

Given this, you’d be surprised how many businesses fail to leverage their data. Actually, you’d be surprised how many businesses fail to even evaluate the potential in their data. In all the ways that matter, it’s like leaving money on the table.

Remember Science Class in junior high, specifically the experiments we performed with inclined planes? You’ll recall that a weight positioned at the top of the ramp is full of potential energy – and has the capability to do work (in this case, roll down the plane). As a business manager, you need to start thinking of your data that way – it’s full of potential energy to do work on your behalf and on your customer’s.

In the IoT space, this topic isn’t just academic: it’s central to the fundamental value equation we bring to our customers. IoT is all about data generation, transport, evaluation, and manipulation. It’s one of the fundamental engines driving this forecast:

Recent growth in the amount of data stored


Forward looking business writers, like Victor Mayer-Schonberger and Thomas Ramge, the authors of “Reinventing Capitalism in the Age of Big Data” are predicting data is going to become an asset in the more traditional sense. Markets will replace valuation based primarily on price with a valuation that encompasses the supplemental data that a party brings to the marketplace as part of the transaction. This is data capitalism – where data becomes a currency. That’s an interesting future for IoT players to be thinking about, given the vast quantities of data they will be generating.

So where do you begin with data capitalism? Given that rising tide of data, we all know examples of enterprises that wind up flailing at the topic, metaphorically drowning in the wave of information. Well, just like early sea-farers that are navigating dangerous waters, we recommend you have a good map.

The Data Map – Inventory and Audit

A data map should be a fundamental tool in your business, just like a general ledger. It should be an evergreen instrument, and it’s likely every part of your data capitalism team, not just the analytics group, is going to have a role in either creation, maintenance, or management of the map. The analytics team is probably the best functional owner, but as stewards, not proprietors. Likewise, business leaders own the business fundamentals we mentioned, and the map should be a strategic asset as they develop their plans to grow the business by driving those priorities.

If you claim to be managing an asset, it goes without saying that you have inventoried that asset, correct? Start here. Do you have a data inventory? Can you reliably claim you understand, across the process map of your business, that you know what data is generated at each node, across each link, and as part of each transaction? Is that data classified as primary, generated by the element, or is it derived from some combination of primary element interactions? Is that data captured, and if so, where is it stored? For how long? What is its periodicity or update cycle? The point here is probably clear – you can’t manage what you can’t enumerate.

Odds are high that you already have process flow maps, and those are a great place to start. Combine them with your physical topology pictures, and create a cross-functional working team that will march their way across those pictures to create a third view – the data map overlay that exists on top of both.

As you already may have surmised, that team needs to include the people who own the elements, the links, the nodes and the transactions, not just the data, analytics, and reporting teams. The goal is to examine each link in the chain, capture all the outputs, and catalog their definitions, details, and depths. Designers, engineers, technicians – all have a role in terms of audit to make sure the data elements are comprehensively documented.

The tool you use to capture this is up to you, but make sure it’s robust, capable of comprehensive versioning control, and, ideally, accessible by all the parts of your organization. Creating a map using an arcane utility that requires expensive licenses and becomes a jealously guarded asset by select owners is not what you’re after here. You want to democratize the data: the model you’re seeking is more of a blockchain – secure, but open, redundant and ubiquitous. You want to train all of your teams to think about data as asset, and that means everybody needs to be part of mapping and managing it, and therefore has to be able to access it.

The idea and philosophy you’re propagating is that all information has value – and you’re not in the business of throwing value away. You want to build a data-centric culture, and you want to train your teams to think that way.

This kind of thinking needs to be baked into your development process – no design is issued without a section that details the data elements produced or modified, and shows how the enterprise data map is altered or enhanced by deployment. Any report produced should, as part of its key, be tied to the data map so that sources are clearly identified. Does this sound like overkill? Think back to that point about money left on the table. Neglect this discipline in the environment of a rising data tide, and don’t be surprised when water starts coming into the boat. You expect your accountants to be able to show your financials to the penny. Bring the same exacting attitude to the task of mastering your enterprise data sets.

Going Forward

Cataloging and auditing your data is the critical and necessary first step to leveraging that asset. Think of the map as the circulatory system of your enterprise, mapping the flow of information rather than oxygen, but delivering the same end result – enabling work, growth, and protecting the health of the business.

(Article by Chris Schultz, a principal at Analytic Marketing Innovations (AMI) and a RUMBLE Strategic Partner. Their solutions delivery approach identifies executable steps and recommends both near-term and long-term courses of actions, helping your business leverage data insights for growth and transformation.)