Without the detective work and data refinement skills to process data, its utility remains largely wasted.
I read a quote recently from the creator of Sherlock Holmes, Arthur Conan Doyle, that reminds me of the situation I often find businesses in when they come to us having realised they need to be doing more with retail and financial-performance data.
“It is a capital mistake to theorise before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
It’s not that businesses don’t have access to data—they’ve never had more of it—the trouble is, without the detective work and data refinement, its utility remains largely wasted. “We want to be more data driven”. “Actionable insight”. “Data driven”.
The fact that modern jargon like this has become common is a sign of our collective awareness of how valuable data can be.
Yet the lack of data literacy in retail and finance reveals that these phrases can be little more than buzzwords parroted as part of hollow corporate narratives.
Nobody’s really at fault here. It’s simply a lack of deeper understanding about how business-performance data can be harnessed.
Reflecting on a Forrester Research report from a few years back, Forrester’s Vice President and Principal Analyst, Brian Hopkins, writes that:
“While 74 percent of firms say they want to be 'data-driven', only 29 percent say they are good at connecting analytics to action.”
And there’s the heart of the problem.
For the masses of data businesses swim in, many have yet to find the right lens and methods for making pragmatic interpretations.
How can this be changed? By connecting analytics with action.
Connecting analytics with action
To explore the answer we need to pose the question: What should your data-to-decisions journey look like?
Building your data-to-decision journey means refining basic performance data into a higher grade of decision-making potential.
The journey can be broken down into three stages of data refinement:
1. Description
2. Prediction
3. Prescription
Once we’re at the end of the data-to-decisions journey, we’ll see that: Description + prediction = prescription:
Let’s start.
Building your data-to-decisions journey
Step 1: Description
Descriptive analytics is the first stop on the data-to-decision journey.
What it does is take the raw business-performance data siloed within source business applications and apply simple trend analysis.
This is the stop many businesses happily remain at, making bold claims of being ‘data driven’. Just for having applied the most basic level of refinement to their data.
Being truly data driven and finding much deeper actionable insight means developing the data-to-decisions journey further.
Building your data-to-decisions journey
Step 2: Prediction
Predictive Analytics takes you one step further along the data-to-decision journey building on the two-dimensional picture provided by descriptive analytics.
Essentially, it contextualizes raw performance data into projected scenarios.
For example, answering questions like: ‘what will our business performance look like if scenarios x, y or z happen? Or don’t happen?’
This is where businesses start to find the deeper wells of actionable insight that builds resilience and agility in decision making.
Building your data-to-decisions journey
Step 3: Prescription
Prescriptive analytics is the final stop on your data-to-decisions journey. This is where steps 1, 2 and 3 combine to become greater than the sum of their parts.
Description + prediction = prescription
At this stage predictive analytics data is layered onto descriptive analytics insight to produce powerful prescriptive analytics potential.
This is when strategic thinking can be steered and fine-tuned in a way that brings competitive nuance to decision-making.
Prescriptive analytics effectively predicts what the optimal business outcomes can be if everything goes right.
Naturally, businesses can’t expect the stars to align perfectly. Not all factors will always come together perfectly.
However, reaching this stage of data refinement allows businesses to safely and confidently decide which levers to pull and how hard to pull them to optimise decision-making outcomes.
Learn how FinWorx adds layers of decision-making insight to your operational data. Or Download the FinWorx brochure here.
Start your data-to-decisions journey
Data-driven decision making in hard-to-predict business environments means building sustainable business intelligence through data warehousing so that you can integrate, store and present data to decision-makers.
With the squeeze of the 2020 crisis likely to be felt for some time yet, finding utility in the crude oil of business-performance data will become a massive competitive differentiator.
