«

The data science strategy – part I

Share

In the world of BI, it is not rare to hear a professional say ‘I want to become a data scientist’ due to the nature of this field being so new and exciting. As an interdisciplinary profession, which requires a candidate to create value out of data, and create it in a way that businesses and staff can understand, this skillset is still very much a work in progress, in the professional realm.

However, more and more organisations are seeking data scientists to assist their Chief Data Officers or their Chief Information Officers in managing data, meaningfully. While the professional BI industry drives this data scientist concept forward, it’s still in its infancy, and the overwhelming nature of data influx needs to be minimised immediately for most businesses. In fact, research shows that more than 2.7 zettabytes of data exist in today’s digital universe, and that is expected to grow to 180 zettabytes in 2025.

Therefore, I wanted to share three things your IT team can do, today, to set up and implement a data science strategy, and start to really demonstrate the value of data. So, herewith are three steps your IT team can follow, to set this up.

Find out: what are the key business drivers for data science

This may assist in establishing whether the organisation needs the data science strategy. Answers to questions such as, do we need data science? What business value could be gained by developing a data science strategy? What are the questions the organisation needs to solve to be more competitive, effective, and proactive? will determine whether data science is suitable to your organisation – as data science is not for every business.

Create the A-team with the data science goals in mind

While it would be easier for the organisation to put up a vacancy post online and go through the process of finding ‘the one’, a single data scientist to do it all – this may be challenging, as the skill is in its infancy and therefore scarce. Even students leaving tertiary with the suitable qualification have not yet had time to gain experience. So, it is better to create an A-team that encompasses different skill sets to carry out the organisation’s data science goals. They must also understand the organisation’s key business drivers, as previously mentioned. What’s more, this team must have the know-how in the business, for example, customer engagement or marketing, data and information retrieval, programming, business analytics, statistics, data mining, machine learning and not just technical insight.

Communication skills is a must for the value of data science

Data science is known as a very technical practice. Very often I find that organisations hire a data scientist with exceptional technical and scientific skill, but this person often struggles to communicate the results of data science effectively. This is obviously not a major set-back, however, it is important to stress that insight provided by analytics (determined by the data scientist or IT team) should be articulated to the organisation c-suite or staff in a way that they can understand, otherwise the value of data and its findings gets lost. Data visualisation and good listening skills are essential.

Concluding remarks

While the data science strategy has a combination of steps, please consider these as the first three that should kick it off. In my next blog post, I will share the last four steps to complete this strategy.

In closing, the data scientist profession is gaining momentum – and I, for one, am thrilled at this and believe there is no substitution for a well-defined and valued data strategy. Data can be an exceptionally powerful tool if filtered correctly and processed strategically. This role requires a unique technical skill set and so I implore businesses in all areas of industry to be patient, as these skills develop and are demanded, be supportive of the growing profession and identify the value that data scientists will hold in the future.

Leave a Reply