It’s a tale as old as mankind. When a precious new resource is discovered, groups of people will try to stake their claim or debate over who the rightful owner should be. Data ownership is no different. Every day companies handling large amounts of data are grappling with this. Often times, the day-to-day handling of data is carried out by two, if not three, teams: IT, analytics, and sometimes marketing.
IT is the necessary safe keeper. Without a team working consistently and creatively to secure the data, the company is vulnerable to phishing attacks, malware, ransomware, etc. In addition, IT usually handles data storage, though that could change as companies become more interested in finding new uses for their data.
Customer data analytics is the star of the cast, developing applications for the data. Over time, these applications have become more and more sophisticated, employing new self-service and user-friendly tools such as AI assisted customer analytics to activate the data in real-time as customers shop online. These teams are relatively new but have become essential to a company’s long-term competitiveness.
Marketing is one of the chief beneficiaries of a company’s data infrastructure. Generally, these teams will use the insights gained from analyzing its data to create more sophisticated marketing campaigns. For example, discovering what attracted, retained or repelled customers can help the company better serve them and achieve enormous gains. This is why marketing departments across many different industries are being transformed by the emergence of big data.
In order for the data to be used optimally, it’s essential for these teams to collaborate and define the analytics which will benefit each business group. Because just as in a good play, each business group has a different (but equally important) role in contributing to the business’ success.
How does your company manage ownership over the data? Tweet at me or email me to share your thoughts and experiences.
Founder and CEO of SYNTASA (a Marketing AI Platform). He has over 20 years of professional experience in the field of analytics, data science, performance measurement & management, and strategic planning.