What do you do when you find information in data that requires feedback from colleagues? The people I work with:
- simply duplicate the information in a chat message or phone call to explain the context,
- insert a screenshot into a presentation for the next review meeting,
- send an exported table (along with added comments and notes) by email,
- or combine the above options in any way they choose.
When I think about this elementary activity within data analytics, I cannot help but notice the enormous potential for innovation. This is because such a way of handling information seems highly inefficient (copying, duplication), unreliable (chats and emails get “lost”), uncontrolled (data leaves the managed system and becomes static), and insecure (where might such a table end up?).
The ability to use data for practical decision-making is absolutely crucial for a data-driven organization. Yet this connection to data is often lost because all those chats, emails, presentations, and exported tables consume a lot of time and energy and become a source of misunderstandings.
No one has time to constantly focus on data analysis. Operational matters inevitably consume 99% of our time, which is why proper notifications and alerts play such an important role.
My third challenge in BI (business intelligence) innovation is to ensure that it is possible to systematically capture and share context and practical actions connected to insights derived from data. No one has time to constantly focus on data analysis. Operational matters inevitably consume 99% of our time, which is why proper notifications and alerts play such an important role. This video shows how we approach this issue.
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The best recipe for the success of digital transformation (DX) initiatives is to combine data with practical actions. Data should not wander through isolated systems, emails, folders, or phones. If we want them to be valuable, they must be actively used.
What the future of BI will bring.
Here are the innovation ideas that, in my opinion, BI should focus on if we want to handle the massive growth in data volume and the need for businesses to transform this data into assets:
- easy data associativity, making it possible to expand the range of information and use better predictive models,
- complete data transparency, helping to build trust and uncover insights into business processes,
- the practical usability of data for decision-making and the elimination of inconsistent and insecure information sharing, which will shorten reaction times for complex business challenges
It is no secret that I am a fan of [Qlik Sense](https://emarkanalytics.com/cs/data-technologie/qlik-sense/?utm_source=chatgpt.com), which I believe comes closest to these innovations. Especially when using extensions such as [Inphinity](https://emarkanalytics.com/data-technologies/inphinity-suite/?utm_source=chatgpt.com).
Fifteen years ago, at EMARK, we selected Qlik as the tool that would help companies fulfill the need to realize the benefits arising from data. There were three main reasons for this, which still apply today:
- the associative engine, which opens up a broader spectrum of business information because it does NOT HIDE excluded data,
- the ability to connect and process any data (yes, including semi-structured and unstructured data),
- impressive query performance and excellent compression, making it possible to process large-scale data in a single tool without relying on a maintenance-heavy and costly stack.
Over time, many features have been added, such as better-looking charts, improved governance, alerts, autoML, automation, and more. Still, I believe that these three things are what truly stood behind the success of customer projects.
When you want to meet customer requirements, it sometimes takes a lot of scripting and complicated SETs. It is true that effective solutions are rarely simple and usually cannot be created by dragging and dropping predefined tool options in various wizards. The whole data world seems excited about Snowflake
and Python, which is to a large extent pure programming, so there must be something to it.
I have discussed with colleagues whether tools like Qlik will still matter in 5 years compared to something like ChatGPT. And whether we, as BI developers, will still be useful. I have no doubt that the answer in both cases is “yes.”
