Towards a DATAcentric Organization

Jack Raifer Baruch
May 13 · 5 min read

The last few weeks, people have been asking me a lot about how to become a company that can leverage Machine Learning and AI. This can be a complex issue and hence, difficult to explain. So, I decided to take everything I learned about Organizational Behavior and Culture and all I know about Data Science to try and come up with a good and simple explanation.

Data today has become the most important resource of any organization, at least for those who expect to still be in the market in just a few years. Although we hear many companies constantly using buzzwords such as “Data Driven”, “Data Based Decision Making” and of course “Artificial Intelligence”, truth is, these are no more than marketing slogans.

A big part of the problem is that there is very little clarity regarding what is a DATAcentric organization, or what are the levels of adoption and use of data that could give us a clearer picture of where we are today and where we should be focusing our efforts and resources.

Why do we want a DATAcentric organization?

Let’s begin with the most basic issue: What are the reasons and benefits for which we should spend our limited resources to focus on data?

I can see three main reasons why data is increasingly important:

1. The capability of collecting data keeps growing exponentially, allowing for more massive data collection, at higher speeds and of different type and size. Also, new models allow for quickly improving the quality of our data.

2. New mathematical and statistical algorithms allow us to create Machine Learning models that keep improving the more data we feed them.

3. Computational capabilities and algorithm optimization for Machine Learning models is growing and improving at an exponential rate.

In short, as we can see above, the more data an organization has and leverages, the better data tools it can build to grow and improve, which allow it to acquire more and better data. This becomes a virtuous cycle that pushes DATAcentric organizational growth exponentially. In simple terms, at each step, these organizations double their stride. If my company today has a two-year delay in leveraging data, in one year, I will be four years behind, in two years, eight years behind, in three, sixteen years behind, and so on.

If a company became obsolete in 10 or 20 years, today it might take a couple of years, and sometimes just months.

Now that we understand the importance of DATAcentrism, the next step is to recognize where we are today, at which level of data leveraging we are. We can visualize this on the following pyramid:

Level 0: DATA Hoarding Organizations

Hoarders are people who never throw anything away because it “might be important someday”, their homes tend to be filled to the brim with useless things. DATA Hoarding organizations are terribly similar, the collect tons of useless and irrelevant data, store it in the worst possible way, generally in files and boxes and forget about it seconds later, but never, ever look at it again or get rid of it. Accessing any of the data is a nightmare, and there is never an actual desire to do so. Even companies who have moved to the digital world can still be Hoarders, where all the irrelevant data is stored in local drives and servers, Excel sheets abound, and each fractured compartment of data is organized on the whim of whomever collected the data.

Level 1: DATA User Organizations

Here we see companies that are using data within the organization. These generally collect basic information such as client names, phone numbers, emails, transaction history and other data that is fundamental for the operation of the company. Nonetheless the data tends to be dispersed in silos either by department, team and even individually. The use of this data tends to be operational, like looking for a client’s contact information or corroborating a payment. Data here is just a basic tool with no underlaying strategy or purpose.

Level 2: DATA Informed Organizations

In these, reports, visualizations, and PowerPoint presentations are common with the unusual appearance of an up-to-date dashboard. They tend to consider themselves DATA driven organizations, since, in the best of cases, they do use reports and analysis to guide decisions. However, the intuition and ideas of the C Suite take prevalence when making those decisions. In short, the company can collect data, in some cases organize it efficiently and decision makers are able to access and use them. This is a good step in the right direction, but a deeper exploration of data is required to reach new levels.

Level 3: DATA Driven Organizations

At this level, we can see the huge benefits of data in the organization. These companies take a scientific approach to data, they have professional’s data scientists, weather in house or initially outsourced, who have an incredible amount of knowledge of how to collect, organize and analyze data towards the need of decision makers and employees in the organization. These companies tend to perform much better than the competition since their specialists are always strategically and proactively searching for and testing new insights.

Level 4: DATAcentric Organizations

Finally, at the top of the pyramid, we have a select group of organizations that can be considered DATAcentric. For these companies, being driven by data is just not enough, therefore they made the decision to make data the center of their strategy. Here, Data Science teams take prevalence and are constantly searching for new opportunities and building automatization and predictive models, from simple processes to decision making. Each of the employees has access to the information they need when they need it and in the way they need it. All the data is integrated with the company’s workflow, marketing, sales, operations, invoicing, purchases, IT, C Suite, each part of the organization has the data tools, models, and specific information they require to exponentially multiply their productivity.

Understanding in what level of the DATA Pyramid our organization is currently at, allows us to comprehend our reality today so we can begin taking solid steps towards the next level.

If your organization needs help understanding where they are today or leveling up their DATA, drop me a note.

Jack Raifer Baruch
Data Science Consultant and Head of Data Science at ADA Intelligence

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MLearning.ai

Data Scientists must think like an artist when finding a solution

MLearning.ai

Data Scientists must think like an artist when finding a solution, when creating a piece of code.Artists enjoy working on interesting problems, even if there is no obvious answer.

Jack Raifer Baruch

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Data Science / Behavioral Economics / Machine Learning / Python / Artificial Intelligence / Neural Networks / Video Games

MLearning.ai

Data Scientists must think like an artist when finding a solution, when creating a piece of code.Artists enjoy working on interesting problems, even if there is no obvious answer.

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