A few days ago, I wrote about the analyst function being dead, which spurred conversations about the emergence of a new breed of analysts. Organizations, with all of their investment in data capture, data generation, and business intelligence, still struggle to use data effectively to make decisions. With the explosion of data over the last couple of decades, the analyst moved away from business and morphed into an IT role. The role became more about writing business requirements and providing reports than understanding data and helping the organization digest meaning from it.
Now the analyst needs to move back to a business role, but with more of a mathematics and statistics background. They have to be curious about improving the business and have the acumen to do it. They need to know how to blend traditional data with today’s non-traditional data feeds from blogs, social media, video, etc. The value of the analyst is back in creating business value through relevance, context, and timeliness.
To achieve this, the emerging role of the analyst requires a new skill set and must:
- Understand how to derive information out of data and present it in business terms – this is perhaps the most important of all of the new skills. The analyst must be able to take a tremendous amount of information and coalesce that information into business terms leading to action.
- Integrate various types of information – as data is coming from all different places and in new forms, it is increasingly important to understand how and when to leverage potentially rich data, and decipher what is irrelevant quickly.
- Design problems with various concepts – the analyst needs a consultative style in which different models are applied to solve ever more complex issues.
- Use technology – with Business Intelligence, Planning, and Predictive Analytic style software becoming easier to use, the analyst needs to know not only how to use these tools, but when to use them.
- Delegate – traditionally the analyst needed to do everything. Now as technology, data sources, and businesses have become more diverse, the analyst needs to know how to farm out some of the analytics to specific expertise at the right time, guide the project, and integrate the results.
The analyst also needs to transform informational projects into a process where requests for information are appropriately managed. This includes breaking down information into four areas: persistent information or basic reporting of facts on a regular timeframe; performance measures that have higher level KPIs; problem analysis; and data exploration.
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Gone are the days where the analyst was a report writer, spending too much time on data acquisition. They must now know how to enhance data to get more out of it in a timely and fashion and present that back to the business in a manner that drives value creation.
