The Path to a Data-Driven State of Mind

Throughout the course of this blog, I have covered quite a wide range of topics focused on the data-driven organization and suggestions for getting the most out of your data initiatives. I wanted to write a post that summarized my thoughts so far in order to provide a baseline from which I intend to expand. If you are just joining the blog now, this post will serve as a recap of our most important discussions.

How Data Culture is Changing the Enterprise

In our first post on the data-driven organization, I set out to establish just what a data-driven organization is and why it is a desirable designation.

I defined a data-driven organization as “one that has begun a cultural journey to define data as its main decisions platform and make the shift towards using data to carefully design metrics to be used as the key driver behind its decision-making process.”

The key point in our definition is that data is elevated above executive insight and intuition, and should be injected into the decision-making process wherever possible. Doing so results in a competitive advantage, which can be seen when looking at the rise of Amazon, which was founded on the basis of using data to determine its direction, as well as modern marketing practices.

You Don’t Push and Agenda- You Inspire One

But how does one begin to shift the culture of their enterprise towards one that sufficiently values and utilizes good data practices? My subsequent post began to lay out a series of expected obstacles and best practices for developing a data-driven culture.

Getting an organization excited about a cultural shift towards being data-driven relies on approaching the problem from two angles: bottom-up and top-down. I identified four key principles that should help an organization ensure its commitment to data-driven decision making is wholistic:

1.  Executive Buy-In
2. Clean collection
3. Comprehensive access, and
4. Easy usability

Logical Vs. Emotional Decision Making

My next post departed from making practical suggestions and instead posed the questions: how do we value human intuition in the data-driven era? What is the logical end-state of a data-driven organization? Is there a place for emotional decision making in our organizations?

Intuition has successfully guided human decision making from the get-go, but data consistently shows us that we cannot trust our instincts in many cases.

I proposed an analogy of an employee who underperforms, but is hard-working, well-liked and viewed as competent. Should data drive the decision to invest in the employee or cut them loose? Right now the question remains unanswered

Don’t Just Collect Data, Make it Work for You

My next post returned to providing practical solutions for common problems associated with implementing a data culture.

In the past, organizations were starved for data. Today, an overabundance of data causes choice paralysis and uncertainty about data validity/relevance. Staying above water in a sea of data requires narrowing your focus to well-defined KPIs and identifying which data points are most relevant to what you are trying to measure, whether it be marketing effectiveness or production statistics.

To help reader’s set up their own KPIs, I proposed the following Rules of Five:

1. The first step is to pick five achievable goals (KPIs).
2. From the data you have available, determine to the best of your ability which five data points will be the most relevant in achieving that goal.
3. Using those data points, develop five measurements that will track those KPIs on a qualitative scale of 1-5(fail to excellent).
4. Track the progress of these KPIs every week for five weeks. This will give you a five-week scoring measure with which you can determine the suitability of each KPI.
5. Repeat as needed until you have found the KPI that most accurately reflects the results you want to achieve.

Is Your Organization a Data Fiefdom?

Data fiefdoms represent an organization where access to data or analytics tools are controlled by one “lord,” and are an interesting way of conceptualizing the problem of siloed data within organizations. Siloed data stands in direct opposition to the goals of transparency and collaboration intrinsic in the data-driven organization.

Still, the management of data is critical and I stress the importance of recognizing the difference between governance and policing of data. For companies looking for ways to avoiding the pitfalls of siloed data while ensuring their data is reliable and relevant, I proposed this five-pronged approach:

1. Solid Data Governance
2. Organizational Data Ownership
3. Cross function, cross-department Collaboration
4.  Adequate Training
5. Valuable Metrics

Will AI Take Your Job? I’d Say Both Yes and No

My most recent post was inspired by thinking about how AI is situated to disrupt the workforce and the anxiety over that disruption present amongst those who recognize how transformative AI will be.

Certainly, AI is going to increase productivity and result in redundancies; we are already seeing multitudinous industries (such a the trucking sector) preparing for such a transition. I approach the problem from the perspective that the change is inevitable, and that our best path is to determine how we can adjust to it.

By looking at the past, specifically how the US economy shifted from agriculture to industry, it is shown that society, in general, is able to handle dramatic shifts in employment due to technological advancement. On a more granular level, AI taking over mundane tasks associated with white-collar industries such as law and accounting may improve job satisfaction by refocusing the positions on creative functions that are more difficult to automate.

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