The message is clear, if you read the press you will hear how data is now the building block of business. New business models are built primarily on data and interfaces rather than on products. Services are tailored using data; customers are profiled, located and sold to using data. If your company can’t harness this data as quickly and as “cleverly” as your competition, things are going to get tough.
Even scarier, fast-evolving start-ups that have data-driven business models have the potential to disrupt your industry and take market share from your business.
Many organisations understand the threats and huge opportunities of better harnessing data, but they are unable to mobilise the people and the skills to build cutting-edge data-driven applications.
Data scientists are in short supply. The technology to build Big Data analytical models is complex and costly. Getting serious about working with massive data sets is a big decision that can be difficult to execute.
Although many organisations have deployed some kind of analytical solution for years, it may not have been “Big Data” driven and it is unlikely to have deployed the cutting-edge machine learning and AI technologies that are now propelling data from assisting decision making to making decisions at speed.
However, there is an irony in the skills gap. The people in your organisation that have been crunching Excel or even working with BI tools to build models and provide insights may not have the skills of a data scientist. They may not be able to write machine learning algorithms, BUT they do understand your business implicitly, and they are the people most likely to know what questions they want AI systems to ask and answer.
Typically, investing in Big Data projects meant bringing in a new breed of skilled data scientists and trying to build bridges between them and the people that have “lived” inside the company’s data for years, like operations managers, business analysts and IT professionals.
If the power of AI-driven analytics could be put in the hands of these people, the results have the potential to be astounding.
Technology is moving fast. Companies like Dell are providing infrastructure to power massive data processing. At the same time, they are looking for companies pioneering new technologies that solve business problems on the ground. The skills gap between existing staff with huge company knowledge and their ability to get deep into data science and machine learning coding is something that Dell EMC saw as a problem. To solve it, Dell has partnered with DataRobot, who have pioneered a technology they call autonomous machine learning.
Without oversimplifying too much, this enables your existing team to build the models that they would normally rely on a data scientist to do. DataRobot runs machine learning algorithms across your data and builds hundreds of algorithms that business analysts can then apply against that data. The existing team can use DataRobot, without the need for data scientists to code on their behalf, for the purpose of quickly selecting the strongest algorithms and producing powerful insights in just a matter of days, as opposed to months. Additionally, DataRobot leverages on best practices and incorporates built-in guardrails so that you can develop highly accurate machine learning models.
One of the keys to transformation is keeping track of emerging technology and leveraging it. The technology is moving at pace, and in many instances, the most important resource will remain your most experienced and knowledgeable employees. Nonetheless, you will need to arm them with the tools (like DataRobot) to achieve extraordinary results.