Great Data Drives Profits

Great data drives profits

great data
Great Data Drives Profits

We live in an age where technology is intertwined with our personal and business lives. Our machines, devices,  and activities on social networks and the web are generating an explosion of data. It is this trove of big data that most executives in commercial or public enterprises believe hold the keys to untold insights. In fact, a study from Economist Intelligence Unit, reports that some 97% of executives believe that unlocking the value from this data is a strategic priority.

This is confirmed strongly in the CIO’s annual survey in 2014 with 72% of respondents stating “data use” as their top priority. To fully grasp the importance of this data, you need to consider what this data potentially represents for the business. And that requires a little journey back in our computing history.

Back-office and front-office automation

The first generations of software were focused primarily on automating back-office functions such as finance, manufacturing, and procurement. Data was pretty much reflected an event or, more accurately, a transaction.

The same was generally the case for the next wave of front-office applications, such as CRM. Data remained a by-product and was analyzed from time to time to provide trends and insights to optimize business processes. The value though was as it helped organizations to achieve the most profitable outcomes. In this age of productivity, we used it to analyze where we sold best, what products we sold most of, what was the most profitable region, division, region, etc.

Enter the age of engagement

The last decade has given rise to new technologies and applications that operated in a different fashion. Social platforms like Facebook or mobile phone data no longer represent transactions or events only, but reflect actual records and behaviors. It shows what you like or dislike – where you are – who are the members of your household or circle of friends. Most people refer to this as data.

This engagement data has potentially massive value when you can combine it with the traditional data landscape. If you are interacting with a company (e.g., shopping on a web site), it can feed you relevant pieces of information at the right time, shaping the outcome in a direction they desire. For example, if you shop on Amazon, you likely start with a regular search. But then things change compared to a normal Google search. Suddenly you get recommendations, reviews, alternative products, add-on products, special offers. And every time you click on a link, ensuing recommendations become more personalized and targeted, with new suggestions and offers designed to lead you toward a desired outcome – in their case a purchase.

At the heart of this experience is data — engagement data has become the fuel to deliver the most engaging experience and nudge the user towards a desired outcome. Of course this is not limited to retail, but is applied in every industry.

Companies are racing to unlock the value from the mass of data they collect in order to build new data-centric products and start fueling their own customer engagements. And to do so faster and better than the competition. But the “Data Directive” study makes a sobering statement in this regard: Only 12% of the executives thought they were highly effective at this and 15% thought they were better than the competition.

The imperative to design for great data

The reason many companies fall short on their strategic intent with data is locked up in our historical approach to data. Data was never the centerpiece of our thinking. The applications were. The relative few applications representing our back-office and even front-office applications looked pretty similar with similar data structures and concepts. But to fuel these types of engagements when they happen requires different thinking and potentially different technologies. The most fundamental aspect is how good the data needs to be to be successful.

Let’s consider two examples:

  1. The Google self-driving car has the same functional systems as a normal car to turn, brake, and accelerate. However, the thing that makes the care drive and steer in a (hopefully) proper manner is data — engagement data — data from the navigation system, sensors, and cameras. How accurate does this data need to be though?
  2. And revisiting our Amazon example, what are the chances of you selecting the recommended product if you already purchased that product last week when you were on the site?

There is an imperative for great data: To be able to design our environment in such a way that we can deliver great data continuously to any place or person that needs it. Great data is simply data that is clean, safe, and connected.

  • Clean: Data that is accurate, de-duplicated, timely, and complete.
  • Safe: Data that is treated with the appropriate level of security and confidentiality based on the policies regulating your business or industry.
  • Connected: Data that links all the silos to reflect the whole truth consistently.

The challenge for most companies is that they can deliver clean, safe, and connected data for a single project at a time through the heroic efforts of scare IT resources. And this process is restarted every time a new request is received.

More often than not, each new project is executed by different teams, using different technologies resulting in poor ROI and slow time–to-value. If we are going to broadly unlock the value in our data to drive the next wave of profits, it is time to think of systematic ways to deliver great data to every process, person, or application. Or risk becoming one of the 85% of companies that lag their competitors in the ability to unlock the strategic value of data.

Some parting thoughts

Consider the following three practical themes as you develop your own great data strategy:

  1. Designing a system of integration that can continuously deliver great data requires you standardize across all your uses cases, all your projects, and all data types — whether on-premise or in the cloud or both.
  2. Establishing end-to-end metadata that is shared across your entire data system. If our data tells us about our business, metadata tells us about our data system.
  3. Designing for self-service and business-IT collaboration. Data is the responsibility of everyone in the company – not just IT.

For more on the imperative of putting data at the center of your organization’s future efforts, read the ebook, Data Drives Profit.