Why Get a Data Executive?
By: Lester Leong
Tags: Leadership, Data Science, Strategy, Chief Information Officer, Chief Data Officer

This article is a case for keeping, retaining, and unleashing the value locked with your data team. Businesses demand data scientists to perform valuable insights, yet fall short in producing expected results. Create measurable and actionable results by aligning your strategy, people, and business with the aid of a digital leader.

The Problem: Majoring in Minor Things

I found an article on Forbes explaining a data science job description below:

Responsibilities: Translate business requirements into machine learning product. Architect and Build machine learning software products for our core business. Communicate with business owners the strategy and rationale, define and execute project plan Lead all aspects of ML automation including model training and development, feature selection and model tuning. Develop production ML and data pipelines. Develop production code and ship to production environments [1].

To those in the data science field, they know that this is a pretty steep ask. Here’s a translation of the job experience or titles needed to do those responsibilities in their respective order:

Responsibilities Translated (Avg. salaries taken from PayScale.com):

Business Analyst ($68,000/year)

Solutions Architect ($117,000/year)

Project Manager ($87,000/year)

Data Scientist ($95,000/year)

Data Engineer ($92,000/year)

Of course, it would be great to hire someone that can perform at least 5 business roles with a 5th of the price. From a business or finance perspective, that is a great investment. The problem is a lot of businesses stepping into data science for the first time tend to have massive expectations. There is nothing wrong with massive expectations, but most data scientists enter a role without proper support to deliver profitable insights.

The Evidence: No Cohesive Vision

From my personal experience in consulting many companies and anyone in IT can attest to, most companies have dirty data (unstructured, missing values, wrong data within certain fields, etc.). This is a big and common problem. It is so common it has a name — GIGO (garbage in, garbage out). For the data scientist, creating predictions from data becomes a very challenging task. Most models require a sufficient sample under various conditions for a model to capture the underlying trend to make accurate and insightful predictions. In reality, these data problems are effecting the results of data teams as seen in a majority of failing predictions [2]:

You know there is a problem, when you hire someone to do X and they give you Y. In this case, make predictions, but instead only tell you what is going on. So, what is really going on? As a data science contributor from Oracle states, companies have the wrong expectations:

“Doing data science and managing data science are not the same [3].”

Basically, data scientists need supporting teams to ensure high quality data flows into their models to create valuable business insights. Yet senior leaders fail to leverage their data scientists to success [4]. The problem lies in a missing leadership seat. For data to yield revenue driving returns, there needs to be a data executive.

The Solution: Data Leadership

A data executive, Chief Data Officer (CDO) or Chief Information Officer (CIO), needs to be placed at the enterprise table in order to execute a business strategy empowered with data. I know some will say that a chief data officer will be suited better for this role, but a chief information officer is fine. The CIO could also perform this task, since it is known that a CIO delivers data driven insights to business leaders. Why not add the requirements to improve data quality into that list? After all, data quality feeds into the same reports, but I digress. The main reason for a data leader is the results for the business [5]:

As seen above, a data executive can unlock the value of data resources and teams within the company to increase profitability by 26% and revenues by 9% compared to industry competitors.

Action Steps of a Data Executive

From a data leader, you can expect at least three major things to occur in your business: a vision with data, organizational buy in for data, and data governance [5]. With those three improvements, data science teams will be able to operate as expected — making valuable predictions.

Create a vision with data

A vision augmented with data explains the why data transformation and what is the end result. For example, the taxi business model was to transport people with cars for a fee. A data augmented vision of the same taxi vision is now known as Uber. A business that transports people with cars for a fee leveraging mobile app technology. The data executive enables businesses to leverage data driven applications and models that were only seen until recently.

Rally the company

In my experience, organizational changes are never easy, but the ones that went better had open and transparent leadership. Also from lean project management, listening to employees and their bottlenecks are possible improvements are a source of operational improvements. A data executive will be the focal point of digital transformation and empower parts of the company to rally into a data driven vision.

Forming data governance

As a manager of many types of people know, people are given their marching orders, but sometimes veer off the main path. This is where data governance comes in. Reporting under the data leader, data governance steers all data to support the business and helps ensure high data quality. Typically, there are governance committees and liaisons built to roll under the chief data officer.

Conclusion

As discussed, 58% of data scientist do not even make predictions with their models [2]. Assuming these are good data scientists, there is an underlying problem — no data leadership. A data executive is required to empower data teams to have the support and vision to add business value. It takes the entire organization to unlock the value from the data that businesses collect and a proper data leader to translate those insights into profits. With a data executive, businesses can have a proper data driven vision, rally the company to the data vision, and establish data governance for increased revenues of +9% and profitability of +26% [5].

Disclaimer: All things stated in this article are of my own opinion and not of any employer.

[1] N. Talagala, Hiring A Data Scientist: The Good, The Bad And The Ugly (2019), https://www.forbes.com/sites/cognitiveworld/2019/04/30/hiring-a-data-scientist-the-good-the-bad-and-the-ugly/#6e36d20b49c0

[2] N. Gift, Why There Will Be No Data Science Job Titles By 2029 (2019), https://www.forbes.com/sites/forbestechcouncil/2019/02/04/why-there-will-be-no-data-science-job-titles-by-2029/#1856b62d3a8f

[3] E. Chenard, Why Are Data Science Leaders Running for the Exit? (2018), https://www.datascience.com/blog/why-data-science-leaders-fail

[4] T. Redman, Are You Setting Your Data Scientists Up to Fail? (2018), https://hbr.org/2018/01/are-you-setting-your-data-scientists-up-to-fail

[5] Capegemini, Leader Transformation, Oracle OpenWorld 2014.