What is the reason why companies still not leverage the full power of data…? 🧐
There has never been greater interest to introduce the use of statistics, intelligent information and science in all aspects of our everyday lives. As a society, we are all obsessed with that stuff.
[…] on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.
Any and all applications of data to anything and everything is justification enough to employ the effort to do so. Business owners blindly invest heavily in data technologies and data intelligence staff in the hope of leveraging their data. But just like joining a slimming club will not make you lose 10 pounds in two weeks, massive investment in DataTech is not the holy grail of decision-making.
More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data.
Organizations become data-smart when their personnel are empowered and trained to leverage the data to make wise data-centric decisions. Those that are data-dumb fall by the wayside, left up to their knees in data, with a defective decision-making process, unfortunate choices and second-rate results.
On the slippery roadmap slope to data enlightenment, companies usually first collect masses of data and store it in a huge data warehouse, then they funnel all the data into a business intelligence tool and finally they expect to reap the insights.
It’s just as futile as throwing a handful of assorted grass seeds into a pot of soil and switching the light off, when what you really wanted were some gorgeous yellow daffodils.
👉 Step 1: First easy step for the techies to extract and collect the data in data warehouse with some nice profiling or modeling too.
👉 Step 2: The chosen business intelligence tool is loaded and all hell lets loose with a non-ending list of requests from users for models, charts, dashboards and reports from all angles and all timeframes.
👉 Step 3: The company waits… and waits… and waits… but where are the insights?
There will be none… just a pile of data and un-used dashboards 🤬!
Left Out In The Dark
In spite of huge investment both in time and monetary terms in data technology, business owners will remain left out in the dark and incapable of making intelligent data-driven decisions. Rather than Artificial Intelligence, Machine Learning, BI Dashboards or Data Warehousing, the state of being data-driven is about users actually asking the right questions. No data technology can replace this.
Being an expert in a subject area, doesn’t mean that you know how to leverage data in that subject. Do subject experts really know what questions they need answered to make an optimum data-driven decision? A very special skillset is required to interpret trends and the statistical significance of data. Without personnel with the right expertise data just sit and gain dust.
Business users are ultimately devastated and frustrated. They are back to square one, but now with a bucketload of futile graphs and charts and no business revelations as to where they should, for example, increase advertorial spend or adjust programming staffing.
But the data intelligence tools have done their job: they have delivered information. It was never said they would deliver business answers.
What follows is a toing and froing between techies who feel their work is not valued and business users who find the work unactionable. Finally, the worst-scenario is that apart from gaining absolutely zilch from the data-initiative adventure, business users stumble on and resort to false insights, which may damage the whole company’s decision-making process, and God forbid, its future.
Example: Old Granny Joan And The Daffodils
Let’s look at the case of the online flower store FlowerLed. Business owner Kevin pours his hard-earned cash into a lavish, custom-built BI tool to analyze each and every customer profile in the hope of targeting his marketing budget at his prime customers, offering discounts and offers at will with no thought to which ones are of real value.
Most-valued customer (MLV) graphs and reports show him that Mr. Loaded is a prized client making extravagant purchases all year round. All communication and marketing efforts should then be focused on him and all those with similar profiles, whichever which way, whichever way how.
Old, house-bound Granny Joan who annually purchases herself a bunch of daffodils is of no interest whatsoever…. But wait, what about her 5 children and 25 grandchildren that send her flowers for her birthday, Mother’s Day and Christmas…? Old Granny Joan is indeed an MLV… but Kevin still doesn’t know it.
Getting To Data-Driven Decision-Making Heaven
Effective data-driven decision-making is effectively down to two things:
💡 What you can measure
💡 What you can do about it
Determining what you can do comes first. Then it’s time for the measures. What’s the point of measures if you don’t know what can be done with them?
If we use the example of weather and climate, weather is something that happens in the short-term, the day-to-day and can suddenly change from one extreme to the next. Climate is the long-term average of the weather over a number of years.
In the case of Flowerled, is the quantity of tulips bought today a real measure for what might be bought in two years’ time?
It is crucial that the measures business users choose have a mathematical and scientific basis. Then it is possible to adjust what can be done to impact the measures and thereby reach data-driven decision-making.
Ready To Be Data-Driven?
To determine if your company is ready to become data-driven an assessment of the level of data application competences company-wide should be performed. This should be done BEFORE investment in any data technologies. Business users must provide an outline of their specific data strategy for each dataset. The outline should respond to:
👉 What result will this dataset affect?
👉 How should the final dataset appear when it is ready?
👉 How answers will be extracted from this dataset?
👉 What will they do in terms of actions on the basis of the results?
The answers will give insight as to whether the team of business users is savvy enough to survive or not without the assistance of a data analyst or data scientist.
When an investment in data technology is ready to be made, there are a wealth of options in the marketplace. Among the best picks are out-of-the-box solutions, like RetentionX, that, with a minimal outlay, help companies to become more data-driven from day one, converting data into clearer actions in simple steps without the need of any BI specialist or data scientist.
How Smart Organizations Become Data-Driven
Once their homework is done, smart organizations decide on their current “data-driven status” and then proceed forward accordingly with some smart approaches:
✔️ Utilizing existing data expertise. Employees with skills in obtaining insights from data can be identified to work with the data and even help others to reach their skill level.
✔️ Forming clear expectations organization-wide. All decision-makers must be made aware of the power and responsibility at their fingertips.
✔️ Promoting knowledge of simple statistics. All users must be able to use and apply data products.
✔️ Establishing one, single data initiative for the entire organization. This avoids confusion and conflict across all levels.
✔️ Identifying and focusing on data that will have the biggest impact. Resources are allocated and business intelligence tools rolled out only where needed.
One final approach worth remembering is to consider abandoning the route to data-driven nirvana. At the outset, not all organizations need to reach it to be successful and can always begin the journey at a later stage of their development.
How Unsmart Organizations Fail
Unsmart organizations often fail by:
❌ Focusing on the technology itself and all it is made up to be with unrealistic expectations about what it will make possible. Using key performance indicators to make decisions at the end of a busy month and guessing at the causes.
❌ Choosing quantity. Choosing quantity rather than quality means all users are happy with a huge range of low-quality datasets that clutter the decision-making environment and hinder the decision-making process.
❌ Placing seniority and legacy before science. A new roll-out provokes a fear of change among users, others don’t want to be made accountable and struggles break out on the best business logic to follow.
A company can progress in leaps and bounds to become data-driven, practice data-driven decisions and discover a whole wealth of fresh business opportunities with the right skills and behavior of its staff, alongside modern data technologies with the appropriate fit.
Those companies that focus on just the technology, shiny objects and buzzwords will fail in their data-driven quest. In the journey from data-dumb, maybe data-consumed and data-driven there may perhaps be the moment when a company finally blossoms to become data-informed.
Ethan Knox, „Is your company too dumb to be data driven“