The year might be more than halfway through, but tech isn’t slowing down. That’s especially true for business intelligence, so here’s a list of the top five things that will impact business intelligence strategy in 2023 and beyond.
If you’ve been on the internet within the past few years, you likely saw this coming. Whether or not you believe the hype, AI will still have a significant impact on BI. For example, AI can power real-time insights into what is happening second by second. This will allow BI analysts to catch anomalies in situ and respond to them much more quickly than before.
Likewise, AI will also be able to spot trends that might escape the notice of even the keenest BI analyst eye. This will then help hone recommendations in a way that was previously unavailable to BI specialists.
In addition, AI will be a powerful tool that can more accurately guide predictions by surfacing unique insights.
This will reduce the time BI analysts spend reworking estimated KPIs and other metrics, giving them more time to focus on other more creative responsibilities.
But those are just a few of the ways AI will continue to shape the BI space. Naturally, there are numerous ways AI will have an impact — ways that probably extend beyond what AI itself can predict.
Natural language processing
While this does seem to fit under the previous heading, we believe it deserves its own section, as its implications are different, especially for BI. Essentially, NLP allows people to talk to digital systems using everyday language, which opens up possibilities in terms of nuance and precision. Many of us have been taught to interact with computers in a conversationally unnatural way that doesn’t always yield the results we’re looking for. If you’ve tried to use a search engine to find esoteric information, you probably know what we’re talking about. After all, many of these engines focus on matching words rather than matching intent. NLP is focused on fathoming meaning, and that’s a game changer.
After all, BI analysts cannot possibly imagine all the myriad ways a stakeholder might want to query the business intelligence dashboards. This means that when a stakeholder wants unexpected, incredibly granular information, they might have to return to the BI analyst to ask them to tweak something so that the right information can surface. With integrated NLP systems, the stakeholder will be able to ask for the data they need without wrangling sliders and twisting themselves into knots, and the info will appear at their fingertips in a matter of moments. No need to go back to the BI analyst for extra help. This convenience and effectiveness will only lead to an increased interest in NLP models.
Traditionally, BI tended to rely on historical data. But, as mentioned in the AI section above, real-time data is becoming increasingly easy to capture. As businesses keep on adopting this streaming approach to collecting and analyzing data, their decisions will become more focused, more responsive, and more effective.
For example, in crisis situations, old predictions lose their relevance. They were made with assumptions that are no longer valid. Previously, without streaming analytics, BI analysts would have had to scramble to reconfigure their models based on the scant new information they could glean. However, thanks to streaming analytics, course changes and cascading recommendations are provided nearly immediately.
This allows companies to stay flexible and agile as they rethink strategies and find new courses of action in response to unexpected situations.
This will also lead to a shift in the tools of the trade. The solutions that are streaming-focused will gain additional relevance. Power BI has great real-time capabilities, so it will be even more crucial to know for BI pros. Power BI was always a strong recommendation from us, but in the future, this tool might become a necessity.
Data quality management (DQM)
In our modern data-drenched world, having good data quality is crucial. After all, if companies are now collecting data in real time, that means there is much more to handle and clean. And making data clean and useful is of key importance because bad data leads to bad business decisions. Abundant data is useless if it just leads to abundant noise. This is where managing data quality comes in.
This entails a few discrete methods of eliminating unhelpful data. For example, duplicate data, outdated data, and anomalies all have to be identified and removed from the sets that will then inform business strategy. Just think about it — duplicate data can give incorrect weight to the information, outdated data can defeat the purpose of real-time analytics, and anomalies can make a business question strategies that are, on the whole, working.
In short, as data volumes increase, DQM will also have to increase. Just having a mountain of data will not be enough — it will have to be made a mountain of good data. Expect DQM to gain more and more prevalence within the BI space as 2023 turns over into 2024.
Data governance and privacy
While this is of prime importance, we are putting this at the end because it impacts everything we mentioned above. Organizations handling people’s data need to make sure they treat it with care and respect, both from an ethical standpoint and a legal standpoint. And that’s not to mention that poor data management can significantly tarnish a business’s reputation and thus its bottom line.
As AI is so new to BI analytics, it can often be a weak link in the data chain, and this means that BI specialists will have to proceed carefully as they begin to integrate it. When opening customer or business data to machine learning models, people in BI must keep data security sacrosanct.
Before the algorithms get the data, it’s imperative that systems are established to ensure that no one else can.
This also cascades to data quality management. Not only does the data have to be clean, it also has to be protected. Depending on the size of the organization, that means that there might even be roles for data stewards whose core responsibilities revolve around ensuring that the information granted to the business is kept secure from unauthorized access. This is especially vital for BI professionals who work in healthcare, defense, or finance.
So expect that these new ways of gathering and interacting with data will also lead to an uptick in the demand for data security and professionals who focus on it.
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