While many believe that the dawn of a new year represents a clean slate or a blank canvas, we simply don’t leave the past behind by merely flipping over a page in the calendar.
As we enter 2021, we will also be building off the events of 2020 – both positive and negative – including the acceleration of digital transformation as the next normal begins to be defined.
As the pandemic took hold, IDC surveyed technology users and decision makers around the globe, reaching out every two weeks until September, when the survey frequency shifted to monthly. These surveys helped IDC develop a model that describes the five stages of enterprise recovery, aligning business focus with the economic situation:
The IDC surveys explored how the crisis impacted budgets across different areas of IT, from hardware and networking, to software and professional services. When the pandemic first hit, there was some negative impact on big data and analytics spending.
However, the economic situation changed as time went on. Digital transformation was accelerated, and budgets for spending on big data and analytics increased. This spending has continued during the return to growth, with more organizations moving toward becoming the future enterprise.
I have long stated that data is the lifeblood of digital transformation, and if the pandemic really has accelerated digital transformation, then the trends reported in IDC’s worldwide surveys make sense.
But data without intelligence is just data, and this is WHY data intelligence is required.
Data intelligence is a key input to data enablement in the digital enterprise, both by improving data literacy among data-native workers and by assuring the right data is being used at the right time, and for the right reason(s).
WHO needs to be involved in implementing and using data intelligence in the digital enterprise?
There is an ever-growing number of roles that work with data daily to complete tasks, make decisions, and affect business outcomes. These roles range from technical to business, from operations to strategy, and from the back office to the front office.
IDC has defined people in these roles as a generation: “Generation Data,” or “Gen-D” for short. Gen-D workers are data-natives — data is what they work in and work with to complete their tasks, tactical and/or strategic.
You may be part of Gen-D if “data” is in your job title, you are expected to make data-driven decisions, and you are able to use data to communicate with others. Gen-D workers also contribute to the overall data knowledge in the organization by participating in data intelligence and data literacy efforts and promoting good data culture.
WHEN do you need to gather intelligence about your data?
Now is the time.
The next or new normal has already begun and the more you know about your data, the better your digital business outcomes will be. It has been said that while it can take a long time to gain a customer’s trust, it only takes one bad experience to lose it.
Personally, I have had several instances of poor digital experiences such as items sent to the wrong address or orders (including mobile food orders) being fulfilled incorrectly.
Each represents a data problem: incorrect data, incorrect data interpretation, or a complete disconnect between the virtual and physical world. In these cases, better data intelligence could have helped in assuring the correct address, enabling correct order fulfillment, and assisting with interpretation through better data definition and description.
Even if you don’t have a formal data intelligence program in place, there is a good possibility your organization has intelligence about its data, because it is difficult for data to exist without some form of associated metadata.
Technical metadata is what makes up database schema and table definitions. Logical and physical data models may exist in data modeling or general-purpose diagraming software.
There is also a high likelihood that data models, data dictionaries, and data catalogs exist in the ubiquitous spreadsheet, or in centralized document repositories. However, just having metadata isn’t the same as managing and leveraging it as intelligence. Data in modern business environments is very dynamic, constantly moving, drifting, and shifting – requiring automated collection, management, and analytics to extract and leverage intelligence about it.
In many English-speaking countries, “Auld Lang Syne,” a Scots-language poem written by Robbie Burns and set to a common folk song tune, is often sung as the clock strikes midnight on the first day of the new year.
The phrase “auld lang syne” has several interpretations, but it can loosely be translated as “for the sake of old times.” As we move into 2021, we need to forget the negatives of 2020, and build on the positives to help define the next normal.