Is Your Organization Capable of Competing in a Data Driven World? – Part 3

Getting Teams Equipped to Share Everything Not Just Some Things.

Technology has been both a cause for the challenges companies experience and the tools for success.  But it is the change in culture in the organization that allow leaders to leverage data driven decisions properly.

At the core of this journey to adaptability lay a yin-and-yang symmetry of shared consciousness, achieved through strict, centralized forums for communication and extreme transparency, and empowered execution, which involves the decentralization of managerial authority.  Together, these power the evolution of a data driven enterprise to use it properly.

Your transformation is reflective of the new generation of mental models we must adopt in order to make sense of the twenty-first century.  If we do manage to embrace this change, we can unlock tremendous potential for human progress.

So here are some of the principles we’ve developed over the years to enable and protect a healthy data driven culture.  I know that when you distill a complex idea into a T-shirt slogan, you risk giving the illusion of understanding – and, in the process, of sapping the idea of its power.  An adage worth repeating is also halfway to being irrelevant.  You end up with something that is easy to say but not connected to behavior.  But while I have been dismissive of reductive truths throughout this series, I do have a point of view, and I thought it might be helpful to share some of the principles that I have found to be most effective in developing a new culture.

“The trick is to think of each statement as a starting point, as a prompt toward deeper inquiry, and not as a conclusion.”

  • Give a good idea to a mediocre team, and they will screw it up.  Give a mediocre idea to a great team, and they will either fix it or come up with something better.  If you get the team right, chances are that they’ll get the ideas right.
  • If there are people in your organization who feel they are not free to suggest ideas, you lose.  Do not discount that ideas come from anywhere in the organization.
  • It isn’t enough merely to be open to ideas from others.  Engaging the collective brainpower of the people you work with is an active, ongoing process.  As a manager, you must coax your staff and constantly push them to contribute.
  • Do not fall for the illusion that by preventing errors, you won’t have errors to fix.  The truth is, the cost of preventing errors is often far greater than the cost of fixing them.
  • Change and uncertainty are part of life.  Our job is not to resist them but to build the capability to recover when unexpected events occur.  If you don’t alway try to uncover what is unseen and understand its nature, you will be ill prepared to lead.
  • Similarly, it is not the manager’s job to prevent risk.  It is the manager’s job to make it safe to take them.
  • Failure isn’t a necessary evil.  In fact, it isn’t evil at all.  It is a necessary consequence of doing something new.
  • The people ultimately responsible for implementing a plan must be empowered to make decisions when things go wrong, even before getting approval.  Finding and fixing problems is everybody’s job.  Anyone should be able to stop the production line.
  • A company’s communication structure should not mirror its organizational structure.  Everybody should be able to talk to anybody.
  • An organization, as a whole, is more conservative and resistant to change than the individuals who comprise it.  Do not assume that general agreement will lead to change – it take substantial energy to move a group, even when all are on board.
  • Do not confuse the process with the goal.  Working on our processes to make them better, easier, and more efficient is an indispensable activity and something we should continually work on – but it is not the goal.  Making the product great is the goal.

The path to a data driven company is one that is achieved through the balance of technology and culture.  The “dark data” that your organization has accumulated over the years is where we have focused our attention.  Illuminating that data and making it useable for your people to make decisions is the goal that comes from building a culture of trust and shared consciousness.

If you would like to learn more about how InCloud Control has helped other companies on this journey please feel free to send us a note at: or call us at (404) 316-0082

Give Your Data New Life

InCloud Control Intelligent Data Solution


Solving the Analyst’s Data Problem

Recently I have been talking to a number of data scientists and business analysts about what they actually do when performing a new analysis of some nature. Their processes were quite surprising because they were far more data intensive and far less modeling / analysis intensive than I had thought.


The Analytic Development Process

Analysts start by thinking about the problem they are trying to analyze.

The next thing they do is go after the data they think they might need. This means determining what data is actually available. Then they work with IT to get access to that data. And finally they pull the data together into some form of a sandbox. They do all of this data preparation work before they start building the analytic model, statistically analyzing the results, interpreting what the results mean for the business and communicating these insights.


More Than Half Their Time Spent On The Data!

The data scientists and business analysts will say they spend over half their time addressing these data related activities. This means they spend less than half their time actually doing analysis!  Does that make any sense?

At InCloud Control, our products and services are excellent at helping enterprises simplify and accelerate access to data. Out-of- the-box today we have products for automatically introspecting data sources, discovering relationships and then modeling them as friendly entity-relationship diagrams that are easy for the analysts to understand.

Once the data is identified, our development studio simplifies the building of easy-to-understand views of the data. Next our powerful information server automatically optimizes queries that required data sets. And then depending on the sandbox strategy (physical, virtual, or hybrid), our server can also manage these data sets. And all of this can be done in hours or days, rather than weeks or months in the “old way” using ETL, data replication tools and/or hand-coding.


Data Virtualization Speeds the Process, and More

With data virtualization the result is a 2-10x acceleration of time-to-analytic results, which pays off handsomely when analyzing revenue optimization, risk management and/or compliance opportunities.

In addition, the data scientists and business analysts are not only more productive, they are much happier because they get to do more modeling and analyzing and less data chasing. And happier analysts are easier to retain, a key issue given the shortage of analysts today.  Further all of this works with Big Data, traditional enterprise data, external or cloud data, desktop data, and more.

Simple, yet powerful and works for any organization’s IT environment.  Lots of value-add and the users like it too. I think your data scientists and business analysts will find InCloud’s Intelligent Data Virtualization a great solution for their data challenges.


Are You A Data Scientist or Business Analyst?

I am eager to continue talking with data scientists and business analysts about their data challenges. Start a discussion track in the comments section below where we can explore things further.