Sherlock Holmes isn’t magic. He takes in clues from his environment that others never notice and combines those clues with extensive historical knowledge to discover insights others would miss. And just like his ability to process data from the world around him, organizations can use business analytics to drive value and insight.
To a great mind, nothing is little – Sherlock Holmes
Anyone could do what Holmes does, but ordinary people don’t have the tools or training to process clues from their environment. Everything they needed to solve the case was right in front of them, but they couldn’t put the pieces together.
Much like those poor clueless people orbiting around Holmes, companies have all the data insights they need to become in data-driven organizations. They just can’t seem to put all the pieces together, and the power and potential of all that captured data insights remain wasted.
Holmes’s ability isn’t supernatural; it’s something that companies can emulate with quality data analytics. It’s all a matter of the who, what, when, where, and why. Let’s take a look at how businesses can learn to uncover insights using business analytics in the same matter as the famed detective does with crime scene clues.
The Why: Asking the right questions
The key to a valuable insight is asking the right question in the first place. The sheer amount of data captured and tracked by companies can obscure insights without a clear agreement on the goal. Just as Holmes can tune out irrelevant information by focusing on the potential “why” of the case, companies should understand how to craft a meaningful question to begin their analysis.
Asking the right questions is an iterative process. Questions need:
- Specificity: Open-ended questions can lead companies astray. Instead, focus on specifics — think “Which piece of machinery on our shop floors is most likely to fail first?” instead of “How does our company reduce downtime?”
- Expert input: Allowing input into potential questions from a multi-departmental standpoint reduces silos and allows companies to hone the right questions.
- Measurability: The question must be measurable based on the data available. If the data isn’t available, a company can find a way to make it so.
The What: Finding the right data
Once a question is crafted, it’s time to settle on the most appropriate data to answer the question. Companies need the most appropriate data source to ensure the right insight. Typical forms of data include:
- Usage data: How do potential and returning customers interact with the product or service?
- Transactional data: Will companies look at live data, or is it more appropriate to comb through historical data?
- Online behavior data: How do potential and returning customers behave based on tools like Google Analytics, webmaster tools, and other marketing tech (Martech) platforms?
The data source is just as important as the question. This critical second step builds a picture of accuracy and provides the foundation for analysis.
The Who: Assigning a chain of responsibility
Data-driven companies no longer relegate business analytics only to the IT department. A data literate organization is a critical next step. This does create some confusion in data analysis. Assigning a chain of responsibility provides the framework for who will find the data, perform the analysis, and present. For anyone else involved, it establishes a clear chain of command for troubleshooting and further analysis.
Companies must also decide who will collect data. Common sources include:
- Public data
- Social data
- In-house data (historical and current)
Each of these sources offers valid and quality data, but some may be better suited to certain queries.
The Where: Tying queries to location
For large companies, whittling down data to certain geographical aspects provides a clearer picture of customers or company actions. In other cases, releasing data from geographical constraints could provide a wide-angle view suitable for making other types of decisions.
Location can have a profound influence on insights regarding a customer base. Location can help organizations better allocate marketing resources, manage inventory, or plan launches. Location data can even help organizations plan better value offerings, predicting whether certain products or services will succeed in a specific market.
Gathering location data includes:
- A location source or signal
- An identifier (i.e., who is generating the data, often anonymized for analysis)
- Relevant metadata
With location data, companies need a solid idea of who is generating the data to determine that it’s their target audience. Still, all personally identifying information is removed to comply with privacy regulations.
The When: Putting time limits on collection
Part of crafting the right question for analysis includes designating a period for data collection. More data can be useful, but it doesn’t always lead to greater data insights. In fact, too much data collection can lead to decision paralysis.
Instead, companies should consider exactly how long to collect data to arrive at a trustworthy conclusion. If the inquiry needs data from just one month period, extending the data collection process to a year may be more overwhelming than helpful. Alternately, capping collection at a month when the question encompasses long-term insights could cause misdirection in final data insights.
Putting it all together
In the data-driven era, companies must learn to craft questions and data analytics for rich insights that direct everything the organization does. Data will soon be a determiner for success, but some businesses may still struggle with what to do after data capture. Using data analytics, not simply having it, is the benchmark.
Adding together the who, what, when, where, and why of data collection provides a solid foundation for reducing mistakes and elevating true insights to the top of the data pile. Like Sherlock Holmes, companies must find the right information down to the last detail and know when to form an insight to tap into the enormous potential data has for directing major and minor company decisions.
Luckily, companies don’t have to become like Holmes themselves. Instead, companies can leverage the right data analytics tools and expertise from trained professionals to uncover real value using business analytics. It’s time to find out how Insyte Global can transform business decisions through quality analytics that rival the insights of Sherlock Holmes himself.
