Getting Serious About Data and Data Science

To implement successful data programs, companies need to shift goals, muster resources, and align people.

Reading Time: 8 min 

Topics

Building a Winning Data Strategy

Building a winning data strategy requires bold moves and new ideas. Creating a strong data foundation within the organization and putting a premium on nontechnical factors such as analytical agility and culture can help companies stay ahead. This MIT SMR Executive Guide, published as a series over three weeks, offers insights on how companies can move forward with data in an era of constant change.

Brought to you by

AWS
More in this series
Already a member?
Not a member?
Sign up today
Member
Free

5 free articles per month, $6.95/article thereafter, free newsletter.

Subscribe
$75/Year

Unlimited digital content, quarterly magazine, free newsletter, entire archive.

Sign me up

Data science, including analytics, big data, and artificial intelligence, is no longer a novel concept. Nor is the important foundation of high-quality data. Both have contributed to impressive business successes — particularly among digital natives — yet overall progress among established companies has been painfully slow. Not only is the failure rate high, but companies have also proved unable to leverage successes in one part of the business to reap benefits in other areas. Too often, progress depends on a single leader, and it slows dramatically or reverses when that individual departs the company. In addition, companies are not seizing the strategic potential in their data. We’d estimate that less than 5% of companies use their data and data science to gain an effective competitive edge.

Over the years, we have worked with dozens of companies on their data journeys, advising them on the approaches, techniques, and organizational changes needed to succeed with data, including quality, data science, and AI. From our perspective, these are the two biggest mistakes organizations make:

  1. They underinvest in the organization (people, structure, or culture), process, and the strategic transformations needed to get on offense — in other words, to take full advantage of their data and the data analytics technologies at their disposal.
  2. They address data quality improperly, which leads them to waste critical resources (time and money) dealing with mundane issues. Bad data, in turn, breeds mistrust in the data, further slowing efforts to create advantage.

Although the details at each company differ, seeing data too narrowly — as the province of IT or the data science organization, not of the entire business — is a recurring theme. This causes companies to overlook the transformative potential in data and therefore underinvest in the organizational, process, and strategic changes cited above. Similarly, they blame technology for their quality woes and failures to capitalize on data, when the real problem is poor management.

We’ve all observed how companies behave when they are truly serious about something — how the goal changes from incremental progress to rapid transformation; how they muster both breadth and depth of resources; how they align and train people; how they communicate new values and new ways of working; and how senior leaders drive the effort. Indeed, it almost seems as if companies go overboard when they are truly serious about something. Amazon’s

Read the Full Article

Topics

Building a Winning Data Strategy

Building a winning data strategy requires bold moves and new ideas. Creating a strong data foundation within the organization and putting a premium on nontechnical factors such as analytical agility and culture can help companies stay ahead. This MIT SMR Executive Guide, published as a series over three weeks, offers insights on how companies can move forward with data in an era of constant change.

Brought to you by

AWS
More in this series

More Like This

Add a comment

You must to post a comment.

First time here? Sign up for a free account: Comment on articles and get access to many more articles.

Comments (2)
Deepak Chowdhary
The article focuses on intra-company data science development in companies. Also very relevant, in my opinion, is how should companies look at adopting third party analytics software, most likely in the SaaS sphere. How should they shape and objectivise their expectations from the product and stay on course with stated features first and focusing on its integration and implementation, building it into the company management processes and program, resisting the oft immediate expectation of customised solutions which often hijacks from the purpose why the product was decided to be adopted in the first place.
Clement GAVI
'Yet today, too many companies subordinate data to tech.'

And this is understandable. Because it is in the era shaped by the technology that data have taken another dimension.