How to better manage and measure data to feed your information strategy

In a world of work dominated by information, you need to be innovative and predictive — not reactive.

The business world is increasingly a world driven by information. Organizations that are best able to give their employees access to the information they need, when they need it, are the organizations that will make better business decisions. And those organizations that can effectively track and measure that data are setting themselves up for long-term growth.

Saying it and doing it are entirely two different things. An information strategy doesn’t begin with your data—it begins by understanding the challenges that come from that data. Those challenges can be many. But if you’re able to confront them head-on, your information can drive a business strategy that’s uniquely well positioned for success.

A needle in a haystack

More information is flowing into organizations than ever before, and this has created a number of issues — source considerations, data storage, measurement and analytics, and much more. These challenges, as well as current limitations within most industries, have created the need to reinvent analytical strategies for output, capture and mail.

Compounding these challenges are the 4 V’s of Big Data:

• Volume: The volume of information in the world has increased dramatically over the past decade. IDC estimates that the world’s data will total 44 zettabytes by 2020, or 44 sextillion bytes. [1]

• Velocity: The rate at which we accumulate information is only increasing. Google now handles more than 4 million searches a minute, and more than 200 million emails are sent every minute. [2]

• Veracity: With more data flowing into organizations than ever before, it’s more difficult to separate the wheat from the chaff. A third of business leaders don’t trust the information they have on hand to make business decisions.

• Variety: This lack of trust is likely because so much information isn’t being measured and analyzed. It’s estimated that the world now creates 2.5 quintillion bytes of data every day, and 80 percent of that data is unstructured. [3]

What I’ve found in my time working with clients over the past 15 years is that data becomes an afterthought, especially during the implementation of a solution. While a great deal of work may go into implementing a piece of software, the reporting itself isn’t considered (or it’s decided that out-of-the-box reporting would be good enough). Ultimately, most organizations find that while they thought they had the right tool in place, they’re not getting what they need from the analysis of the associated data points.

Data transformation

In order for an organization to move beyond reactive operational analytics and reporting and move to predictive modeling and innovative solutions, it must first transform how it interacts with three types of information.

The first is output data. In the simplest form, it starts with information, then a decision occurs by a human to apply that information. The key is to understand the 4 W’s (who, what, when and where) so you can drive to the “why.” The “why” will be your driving force to cost reduction.

Cost reduction may come in a simple form such as output redirection to a device with a lower Total Cost Ownership (TCO) or output reduction with eliminating abandoned, repeated or unnecessary print. The greatest impact will come if you are able to use the output data to understand how it impacts your business processes. Impacting a business process with enhanced information delivery is where your employee productivity will be impacted the most and where you will see the greatest cost savings.

The second is mail data. Printed Information that has gone through a distribution mechanism not only introduces an array of data points but can unlock big savings. It starts with the receipt of a mail piece, the intake source. This is where the mail data journey begins. We then get into other areas: Where did it come from? What is it? Where does it need to go? Once it gets to its destination, what is done with it?

These are all relevant questions when looking at an intelligent mail strategy with analytical capabilities. The cost savings opportunities stem from the summarization of data points during the journey of that mail piece.

Finally, there’s the capture of data. Having a data strategy on the capture process is critical. You need to understand the source of the document, how it is being stored and where it is being used. Understanding these data points and feeding them into a strategy will lead to workflow refinement, data storage refinement and information handling efficiencies – all of which will drive cost savings.

Transforming this data must be part of your organization’s overall information strategy, as it allows for critical business decisions to be data-driven, rather than intuition-driven. Whatever your strategy may be, it should include these four points:

• A visualization layer to accommodate user preferences, business requirements and accommodating your overall analytics strategy

• Effective strategies using data to validate business processes and to guide process re-engineering

• Analytical governance policies and procedures

• A strategy to report upon process and behavior changes to ensure user adoption is maximized on a go forward basis

The ultimate goal of this process is to accommodate data-driven business decisions to refine your business processes, increase efficiency and boost productivity. To get there, you not only need to capture, access and make use of information, you also need to effectively measure and analyze that data—plus the data you’re not currently measuring and analyzing.