Taking Analysis to the Next Step – How to Become More Valuable to the Business
By Sid Adelman
This column is being addressed to you, the analyst so let’s first define the role of an analyst. An analyst looks at data and produces information – hopefully actionable – running queries and reports, of ten using powerful and sophisticated tools such as Cognos, Business Objects, Microstrategy and SAS. The analyst can report to IT or report directly to a business sponsor.
Your business sponsor has given you requirements for a query or a report. You produced the report or have executed the query and delivered it to the business sponsor. At this point you can walk away and let the business sponsor decide what to do with the results – after all, that’s what most analysts do and that’s all you have been asked to do – or you can take some additional and meaningful action. This is the time at which you can provide added value to the business user first, by fully understanding the results yourself. You needed to do this anyway because you knew how the data was transformed, filtered, aggregated and summarized in the ETL process in your procedure to validate the results – and then identifying particulars or caveats in the report or query that are relevant to the business sponsor. For example, if some of the data is questionable, you identified the results as possibly suspect and also indicated that the data has been accessed from a certified source. You also expressed how the results were validated.
Wait – something has been left out. You were given the requirement for the query or report but did you ask the business sponsor what he or she was looking for or what they were expecting to see? Knowing this might have changed your approach or have kept you from producing information that has no value to the business sponsor. Did you collaborate with them about what source data you were anticipating accessing? Knowing their assumptions about the source data could minimize producing embarrassing results. In the collaboration process, your suggesting better and more extended data sources could also lead to more relevant information.
You are now in a position to identify new opportunities for information which would normally mean executing additional reports or additional queries. There are two options here for taking action:
- You can take it upon yourself to run these queries without first notifying the business and then presenting the results if they appear to have interest or value. This has the benefit of the business sponsor being pleasantly surprised by your initiative.
- You can meet with the business sponsor and explore query options. This approach, which I prefer, places in you in a partnership position with the business sponsor and also gives you additional insights into the business sponsor’s issues, opportunities, and thought process. However, this option should only be attempted if you have proven yourself and already have a good working relationship with the business sponsor.
It would be presumptuous to take either of these two steps without first fully understanding the data that is being accessed. This is where metadata comes in. Hopefully, your organization has at least identified the source data, documented the transformation performed in the ETL process and identified that the data comes from a trusted and certified source. You must also have clear definitions of what the data means and that those definitions are the terminology conventionally used by the business. You need to know that the data is timely as well as knowing the quality of the data, especially if there are problems with the data such as data that is missing. You should also know what other data, both inside and outside the organization could be accessed to provide even further information. You should also know the costs of accessing external data.
There’s more. While you routinely have been looking at and reporting what has happened in the past, perhaps evaluating trends using historical data, there are opportunities to try to predict the future and suggest courses of actions based on predictive analytics. For example:
- What if we raise subscription fees? How many customers would we be expected to lose and what are the characteristics of those customers and what will be the impact on our costs and revenue?
- What if our primary competition raises their rates by 10%, what might it mean for our ability to steal these customers away from our competitor?
- If we have a promotion for one of our slowly moving products, how many could we expect to remove from our shelves?
- What if we outsource our customer service representatives? How much will we save, what will it mean to our customer satisfaction metric and how many customers will we lose?
You should be looking for opportunities to evaluate these “what if” possibilities. While there will be many dead ends with analysis that generates no valuable results, there will be others that will initiate actions that will have a significant impact on your organization’s bottom line.
You might also consider data mining which does not involve the normal hypotheses common to queries but, in some types of data mining, looks for patterns in the data that had never heretofore been considered but could present on opportunity for effective action. My favorite example is a data mining application that identified credit cards being used at a gas pump for a dollar or less and that card being stolen. There had been no hypothesis proposed for this outcome – it was exposed through data mining.
And now for the subheading of this piece: By being proactive, proposing and creating new ways of looking at the data, by expanding the scope of data sources, by extending into predictive analytics and data mining, you have made yourself far more valuable to the business. Taking the next steps will make you far more valuable to the organization, should distinguish you from the pack and should elevate you to the enhanced role of partner with the business user and to the rest of the organization. Elevation to the role does not come about easily or quickly. It’s a gradual process that involves delivery of relevant and important information to the business users. It involves being thoroughly familiar with the business terminology and the workings of the business unit you are supporting. But be careful not to presume you know more about the business than the business people – they won’t take kindly to such an attitude.
About the Author
Sid Adelman is a principal consultant with Sid Adelman & Associates, an organization specializing in planning and implementing data warehouses, performing data warehouse and BI assessments, and in establishing effective data strategies. He is a regular speaker at “The Data Warehouse Institute” and IBM’s “DB2 and Data Warehouse Conference”. Sid chairs the “Ask the Experts” column on www.dmreview.com, and has had a bi-monthly column in DMReview. He is a frequent contributor to journals that focus on data warehousing. He co-authored one of the initial works in data warehousing, Data Warehousing, Practical Advice from the Experts, and is co-author of Data Warehouse Project Management with Larissa Moss. He is the principal author of Impossible Data Warehouse Situations with Solutions from the Experts and his newest book, Data Strategy, was co-authored by Larissa Moss and Majid Abai. He can be reached at 818 783 9634and firstname.lastname@example.org. His web site is www.sidadelman.com.