This is what data analysts should focus their attention on in the new year.
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Artificial intelligence (AI) and machine learning (ML) give analyzes new functions, and companies demand more analyzes that are in real time or can predict the future. The needle has gone further than dashboards and periodic analysis reports. Given this dynamic, data analysts are focusing on their efforts in 2020.
SEE: Building an effective data science team: a guide for business and technical leaders (free PDF) (TechRepublic)
1. Board business priorities
Executive management and every business unit within the company have strategic plans and goals that they want to achieve in 2020 and beyond. It is important to meet them at the beginning of the year to discuss how analysis can best help them and what types of analysis they need; before you meet, you will receive copies of their strategic plans. Update your work log, eliminate initiatives that are no longer needed and update them with the new priorities.
2. Focus your attention on trend analysis
Companies outgrow their first satisfaction with daily, monthly and annual analysis reports; in 2020 they expect more guidance from analytics in determining future business directions. Predictive analyzes and analyzes with a longer trend that can predict the future and contribute to strategic planning will be popular items in 2020; the more you know about trend analysis, the better you will be able to meet the needs of the company.
3. Know the analysis options of your suppliers
Companies choose supplier software because it is a proven commodity and it relieves the requirement to develop your IT from scratch. Sellers understand that companies expect analytic reports as part of the software and that you must understand these reports – depending on your assignment. You may be able to easily configure vendor reports to meet important business needs so that you cannot completely redevelop the reports.
4. Improve your working knowledge of data science
Most data analysts have a background in IT instead of data science, which can lead to communication problems with fellow data scientists. You have to make it a goal to learn more about data science by following a course or just learning more about the subject; this helps you build relationships and helps the company get the most out of data science.
5. Investigate the potential of analytics, AI and ML
Artificial intelligence (AI) and machine learning (ML) have started working together with analyzes in the past two years, but 2020 will mark the further fusion between these three disciplines.
If your analysis can be stimulated by the enormous amount of information that AI can analyze, the next step is to find a suitable mix between standard analyzes that work on data, embellishment of business insights from AI and ML and infusions of people intelligence and creativity that none of these other methods. Investigating and determining how all these different insight-growing approaches can be combined for performance will be a new limit for data analysis.
6. Insist on clean and secure data
Now that technologies such as Internet of Things (IoT) are being applied, there is greater concern about clean data and data security. Companies will still pass these concerns on to security specialists and auditors, but you also need to keep security and clean data first in project requirements.
SEE: 6 ways in which data analysis helps the company move forward (TechRepublic)
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