Tips for your Data Management and Analytics strategy in 2020
Are you working in the domains of data management and data analytics? Here are some strategical steps that you should include in your vision for 2020 and beyond:
Make Machine Learning explainable
Yes, we get it. Machine learning algorithms, neural networks, data science – these are all domains of individuals, who are highly intelligent. But guess what!? So are the responsible managers, business people, product owners, associates, and most importantly – your CLIENTS. They need to be able to follow without an undergraduate degree in computer science, and a PhD in statistics.
Non-experts need to understand your models
Explainable machine learning models and AI are the future. The wider public is learning about potential biases hidden in the data. As a result, you need to explain in plain English how your model works and what are its pros and cons. Take, for example, a marketing campaign. Your clients will want to understand why a certain social group is a better target than another (e.g., single bachelors with an Asian background over divorced mothers from urban areas).
Data Transparency
GDPR, Edward Snowden, the Cambridge Analytica scandal. We live in a world where users struggle to control their data and what is done with them. Provide maximum data transparency for your clients and your business will stand out. At least in Europe, the legal trend will head in this direction. The sooner you prepare your business infrastructure for this, the greater your competitive advantage will be. Your clients will want to know that their data is in safe hands. This leads to the following point:
Invest in a solid Data Governance & Data Hub
Access your data fast
What is a big problem is the current speed of data access. Many businesses have managed to collect data quite well. The next stepping stone towards a truly data-driven enterprise is the ability to connect data fast, regardless of the topic, data type, or the department responsible.
Combine the powers of a Data Warehouse & Data Lake
In a typical set-up, data warehouses are optimized for repeatable processes. They excel at providing answers to analytical questions. On the other hand, data lakes are suited for storing raw, inaccurate data. A data lake is a great source for data exploration and posing better questions. Ideally, your data governance should combine the power of both and have a proper Data Hub strategy. Both your data science and business intelligence teams will thank you.
You might also be interested in our article “Best Online Resources for Data Science”.
Invest in a solid data strategy that will support these goals NOW!