By Gil Nizri
The digital transformation shift from a traditional way of operation and management to one that is both modern and technology oriented has become an imperative for businesses of every size. However, to deliver a good digital business experience for customers and employees requires use of new tools and a change in company culture.
Developing a team of DIY data scientists will be a key to success in this new era.
In the past, companies hired two sets of analysts. Business intelligence professionals examined and reported on what happened in the past. Data scientists wrote sophisticated algorithms to help determine what could be expected in the future. Today, companies need immediate access to predictive analytics to optimize their resources as they make decisions and take actions for the future. And they do not have the luxury of waiting on their data scientists’ slow, complex and expensive processes to deliver algorithms that could become outdated shortly after their development.
Today, companies need automated algorithms to identify patters and provide insight without the burden of too much human intervention, sophisticated technology integrations or even the lost time it would take to re-tune predictive models as dynamics change.
Automated algorithms offer companies predictive analytics, without extensive financial or human resources investment; smart algorithms, the basis for pervasive predictive analytics, is no longer a luxury. The people to drive the use of these automated algorithms are DIY data scientists (CDSs).
Gartner defines a DIY data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.
The ideal CDSs are subject matter experts (SMEs) – risk managers, marketers, heads of operations – who already know what questions to ask and what answers they need to do business. According to Gartner, the CDS can bridge the gap between mainstream self-service analytics by business users and analysts and the advanced analytics techniques of data scientists.
Of course, the transition from data science to citizen data science is not without obstacle. The first challenge could be the SME his/herself. SMEs might be resistance to change or require technology training or support.
Additionally, many companies continue to hold back due to factors like lack of budget, siloed teams and a lack of internal knowledge.
Companies cannot and should not hold back anymore.
Now is the time to upskill your SMEs and embrace the digital transformation, augmenting the analytics upon which your company relies.
Automated predictive analytics managed by CDSs is not science fiction and it’s not the future. It’s now.
Gil Nizri is the founder and CEO of DMway Analytics – www.dmway.com.
