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1. Learn the basics of the visual analytics tools used by the business analysts in your organization. Follow the process of how a real-world project is executed. Solving a typical business problem will give you a chance to experience firsthand what users are doing. You will be surprised at how the tools change your view of the data warehouse and “proper” data structures.

We’ve had many data professionals attend our analytics workshops. Even those with years of experience in the field tell us that managing code and databases is a completely different way of thinking about data compared to analyzing an issue, which has drastically different constraints and goals. Investigating a real problem that the business is facing should help you to see many possible ways that your data stores can be adjusted to enable successful analysis.


2. Find an ally in each of your key business areas, preferably one that is an expert analyst for a viewpoint from “the other side.” Leverage these analysts for invaluable knowledge to design better data structures in the form of tables, graphs, and system maps in your data systems. This is far more effective than decoding the whole process by yourself. When building data warehouses and downstream analytic data stores, we’ve discovered that expert analysts are often excited and motivated to collaborate on improving the efficiency and value of the data sources in their analyses. Unlike traditional BI projects, data projects are now a journey with many twists and unexpected turns. Working closely with business allies that understand the data teams and the business are key to success.

3. Commit to the reality that self-service data management with desktop spreadsheets and databases among business users is not going away. Instead, it will only continue to accelerate over the next few years. Part of this reality is driven by the fact that the appropriate data structure is often dependent on the analysis problem at hand. Another reason driving this growth is that more data streams are flowing into organizations, often at a rate that is overwhelming for analysts and data teams alike.

Seize the opportunity to be more successful in your career as a data professional by understanding and incorporating the new landscape of BI and visual analytics into your data warehouse and collaborating closely with business users to establish a strong environment for analytics. Ultimately, data warehouses are about making better decisions in a timely manner, and these suggestions can help you further the utility of your data warehouse. Excel has been bashed by statisticians and data teams for years. However, it’s a powerful tool for one-off data review and rapid cleansing of data for an urgent analysis. It’s also ubiquitous, both in presence and knowledge amongst business analysts. Tread carefully if you think you can "take it away". You can definitely reduce reliance with better tools, training, support and evangelism.

Stephen McDaniel is an Chief Data Scientist at Freakalytics, LLC and author of several books on analytic software. Eileen McDaniel, PhD, is author of The Accidental Analyst and Director of Analytic Communications at Freakalytics. Both work with clients on strategic analytic projects, teach courses on analytics and are on the faculty at INFORMS.

Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here’s a top new story worth reading and that we considered noteworthy enough to add commentary and analysis by Freakalytics (in purple). A summary of the article and excerpts that I comment on are in black.

In this commentary and analysis, we cover the growth of Tableau and QlikView, the opportunities that exist for Microsoft to disrupt the second-generation business intelligence market and how self-service data integration will likely make data scientists & data enthusiasts much more productive- enabling wide swathes of Accidental Analysts to quickly answer tactical business questions.

The dust is finally beginning to clear from the big data explosion, which is a good thing. One of the problems with big data is that it’s been led by technology, not business requirements. And business requirements will be the focus in the 2014 business intelligence (BI) ecosphere—to enable enterprises to achieve results with data mining and analytics and to prove those results.