Data analysts: Follow this game plan in 2020

This is what data analysts should focus their attention on in the new year.

Business Strategy Concept

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Artificial intelligence (AI) and machine learning (ML) are infusing analytics with new capabilities, and companies are demanding more analytics that are in real time or can predict the future. The needle has moved beyond dashboards and periodic analytics reports. Given these dynamics, this is where data analysts focus their efforts in 2020.

SEE: Tech Predictions For 2020: More must-read coverage (TechRepublic on Flipboard)

1. Get on board with business priorities

Executive management and every business unit within the company has strategic plans and goals that they want to meet in 2020 and beyond. It's important to meet with them at the beginning of the year to discuss how analytics can best help them and the types of analytics they will need; before you meet, get copies of their strategic plans.. Update your work log, elimine initiatives that are no longer needed, and update it with the new priorities.

2. Turn your attention toward trends analytics

Companies are outgrowing their initial satisfaction with daily, monthly, and annual analytics reporting; in 2020, they will expect more guidance from analytics in determining future business directions. Predictive analytics and longer-trend analytics that can foretell the future and feed into strategic planning will be popular items in 2020; the more you know about trends analytics, the better you'll be able to meet the company's needs.

3. Know your vendors' analytics capabilities

Companies opt for vendor software because it is a proven commodity, and it offloads the requirement to develop your IT from scratch. Vendors understand know that companies expect analytics reports as part of the software, and it's incumbent upon you to understand these reports--depending on your assignment. You might be able to easily configure the vendor reports to meet important business needs, saving you from developing the reports from scratch.

SEE: Building an effective data science team: A guide for business and tech leaders (free PDF) (TechRepublic)

4. Improve your working knowledge of data science

Most data analysts have a background in IT rather than data science, which can lead to communication challenges with fellow data scientists. You should make it a goal to learn more about data science by taking a course or just educating yourself more about the topic; this will help you build relationships and help the business get the most it can from data science.

5. Research the potential of analytics, AI, and ML

Artificial intelligence (AI) and machine learning (ML) began to operate in tandem with analytics over the past two years, but 2020 will mark the further amalgamation between these three disciplines. 

If your analytics can be supercharged by the vast amounts of information AI is able to analyze, the next step is to find an appropriate mix between standard analytics that operate on data, embellishment of business insights that come from AI and ML, and infusions of human intelligence and creativity that none of these other methods capture. Researching and determining how all of these different insight-growing approaches can be blended for performance will be a new frontier of data analytics.

6. Insist on clean and secure data

With technologies like Internet of Things (IoT) being adopted, there are greater concerns about clean data and data security. Enterprises will still consign these concerns to security specialists and auditors, but you should also keep security and clean data at the forefront of project requirements.

SEE: 6 ways data analytics are advancing the enterprise (TechRepublic)

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