The two most common growth strategies for business are organic and acquisition. However, that is quickly changing. Companies are beginning to leverage a third option – growth through insight. “Insight” is what is derived from understanding external conditions, internal conditions, and harvesting internal company data. Companies are taking up the charge to educate their employees on the power and possibilities of data and its contribution to the company’s strategic plan as well as short term operational improvements.
Becoming A Data Driven Organization
In this modern, technology-driven age, it is amazing how many key business decisions are based upon gut instinct. There are generally two reasons for this. Either, a charismatic leader has always made the decisions on where the company or division needs to go next, or decision-makers don’t trust the company’s internal data (garbage in – garbage out).
Although the first example may be experience/ego-driven, the second example centers around having trust in the data which can be addressed through establishing data governance processes and data integrity testing. Is data being entered the same way each time? Are there processes around how data is entered? Has the data been properly challenged and vetted? Once data integrity has been established, then the move can be made from “data” to “information”.
The aforementioned move from data to information may be a bit of an oversimplification in that many companies struggle with integrating data technology into their existing systems and business models. There is a wide variety of analytical tools on the market, each containing its own nuances and challenges. Beyond the choice of applications, their integration, and an overall culture shift to make decisions based upon data, the goal is to derive insight. Software alone can’t deliver insight, culture change can’t deliver insight, integrating information sources can’t deliver insight. The most important ingredient in a successful business intelligence internal practice is the role of the “data detective”.
The Role Of A Data Detective
A data detective is a combination of business strategist, database expert, data analyst and sleuth. They proactively look for anomalies in the data, question everything, create hypotheses, run data models, test data integrity, derive conclusions and present “Insight” to the business.
First and foremost, a data detective has a holistic understanding of the business from an operational standpoint, an IT infrastructure standpoint, a sales and marketing standpoint and a strategic standpoint. Only with a solid grasp of the underpinnings and strategic goals of the company can they truly be effective.
Second, the data detective must be able to communicate at the business level and convert questions and issues raised by the business into technical requirements from which information can be derived. They also have to possess a strong technical prowess to know where to look for the answers and how to extract those answers from potentially mountains of Big Data.
Third, they need to be adept at applying scientific processes to finding answers via creating data models, developing hypotheses, testing data integrity and arriving at fact-based insight.
The Difference Between Data Detective, Data Scientist and Data Analyst
Often times, analysts are embedded in specific departments which limits their exposure to other areas of the company. Business Intelligence initiatives can span across all departments. Therefore, an overall perspective of the company (seeing the big picture) is needed. Data analysts are more closely tied to IT in terms of accessing and providing data in the form of reports supplied to business users.
Data scientists on the other hand usually don’t interact directly with the business and are focused more on discovering Insight from Big Data, more hypothesis testing, and may not have the social skills to communicate at the C-level. Techopedia.com defines the role of a data scientist as follows:
“Data scientists generally analyze big data, or data depositories that are maintained throughout an organization or website’s existence, but are of virtually no use as far as strategic or monetary benefit is concerned. Data scientists are equipped with statistical models and analyze past and current data from such data stores to derive recommendations.”
The data detective is closely related to these roles, however, they understand both business goals/issues and processes as well as IT systems. They can create front-end reports and write raw SQL to pull data from databases. They can comprehend data models and their relationships as well as embrace business strategy and objectives. Data detectives have a unique skillset that is not dependent on technology. They are rooted in technology, but well-versed in business.
Data detectives are generally given a vague question by the business and then set loose to try and derive an answer. They are self-directed and leverage information from both the business side and the IT side to develop and prove a hypothesis. Their goals are to uncover missed business opportunities, discover new business opportunities, recommend changes to the data model, resolve data quality issues, improve product/service profitability, recommend sun setting applications, products, facilities, etc. as well as improving the success rates of marketing strategies, pricing strategies and regional sales strategies.
Data detectives may be titled “Business Intelligence Specialists” or “Information Management Specialists”.
Case In Point – The Pricing Sweet Spot
A New Jersey-based building materials manufacturing company wanted to determine its pricing sweet spot since its sales figures had flat-lined. The directive to the data detective was simply “We are not winning enough business deals. Find us the pricing sweet spot for each of our core products.”
The data detective started with compiling data to compare the closing ratio percentage against the year, quarter, market and sales regions. Initial findings were that pricing was pretty normalized across the majority of the sales regions sans one. Next, the data detective created a hypothesis that “the product pricing sweet spot already existed for the majority of the products and the ‘win’ rates for the other regions were much higher than the one, largest region.” The data detective then faced the question: Why was this one region out of alignment? Where was it out of alignment (by product, by quarter, etc.)?
The data detective then went on a second data inquiry. This time to calculate an aggregate average price per product and combine this metric with quote status (won vs. lost). Hypothesis was then converted to insight when he determined that Sales Region C had some products priced too high in comparison to the average “won” quote price point of the other regions. In presenting the insight to the business, the data detective stated that if Region C adjusts their future quotes for specific products to stay within 5% of the average price of the other regions, more “won” quotes will result for Region C.
As it turned out, the solution to the issue was not having non-competitively priced products across the company but rather one large region being out of alignment. A true data detective knows the question asked by the business may not be the right question to answer.
In this particular case, the data detective utilized a Teradata database for back-end infrastructure and Tableau as the front-end reporting tool.
Transforming Information Into Insight IS A Growth Strategy
Decisions based upon gut instinct versus data-driven will continue to be a battle for the foreseeable future. Not all business executives will trust their data and some may even feel in competition with data for decision making.
It costs a company a great deal in terms of investment to launch a new product or service. If their data could tell them the best customers to sell it to, at what price, during which season, with what types of incentives to bundle, why not use that information?
The data detective should be the business executive’s greatest internal strategic tool. They can provide the executive the insight necessary to make the right recommendations or decisions.
Tony Streeter is the Chief Marketing Officer, SVP at Y&L Consulting, Inc. in San Antonio, Texas. Mr. Streeter has led new product development, Ecommerce marketing, and integrated platform marketing initiatives for major companies such as Harland Clarke, Deluxe Corporation and RR Donnelley. Currently, Mr. Streeter leads marketing and branding initiatives for Y&L Consulting, a comprehensive IT Services & Solutions company specializing in IT Development, Information Management/BI, and Service Desk Services.