It’s no secret that the practice of data analytics has come to play a monumental role in the way that modern industries develop in the 21st century. The analysis of performance data across all aspects of business, from consumer behaviour to digital engagement trends, can be used by modern corporations to map their business growth strategies in accordance with their industry landscape. A key element of collecting data is developing AI algorithms designed to filter out valuable metrics in order to tailor-make performance reports on behalf of a business or industry.
Although AI technologies enable the practice of data analysis to be as far-reaching and in-depth as it is today, these two phenomena are by no means interchangeable. In fact, there is a great deal of difference between AI technologies and data analytics as a discipline, both with regards to their application, affordances, and the role that they play in modern business development.
So how can newer generations of data analytics professionals take full advantage of the potential for innovation that AI presents? And how can they determine where the limitations of this symbiotic relationship reside? Today, we’ll be outlining some of the major overlaps and shortfalls in the unique relationship between AI and the practice of data analytics.
Driving the predictions in predictive analytics
One of the driving forces behind data analytics is being able to detect patterns in defined data sets. As you may imagine, pattern recognition is a core component of predictive modelling in data analytics. This is primarily due to the fact that predictive models for data analytics use patterns derived from historical data in order to forecast industry trends.
Thankfully, if there’s one thing that AI technologies can excel at almost organically, it’s recognising patterns and anomalies in complex data sets. Through analysing the metrics that make up business data, AI analytics software has the potential to pick up on recurring elements within those data sets. These recurring patterns can then be used for not just predictive analytics, but also prescriptive analytics as well. How so?
As these same patterns can be observed over other industries or even seasonally, the solutions to rectifying any ebbs or flows in your business data sets can naturally be found in your historical data. In other words, pattern recognition can be used to both identify trends and how best businesses can capitalise on them, as well as how best to respond to recognised growth barriers.
A good universal example here is meteorologists observing fluctuations in atmospheric pressure in order to determine the severity of oncoming storms. By using forecasting programs that are developed to recognise recurrent patterns in the atmosphere both locally and within a certain radius of a defined location, meteorologists can accurately predict future weather conditions within a reasonable doubt. And this isn’t the only example of pattern recognition and predictive analytics driven by AI technologies being prevalent in our day-to-day lives!
Automating data collection
Alongside equipping businesses with the ability to better understand data as well as detect patterns in gathered data sets, AI technologies also boast the potential to automate the data collection process in more ways than one. For starters, AI algorithms can be designed with pre-established parameters in place to ensure that only relevant performance data is added to your business’ research database. Data analysts can set up processes that harvest all data relating to your business’ defined key performance indicators (or ‘KPIs’). In doing so, data analysts can present business owners with data research that directly addresses the pain points and growth opportunities most pertinent to their business and wider industry.
The automation of data collection is also invaluable for businesses working with particularly larger, more complex data sets. By simply factoring a business’ KPIs into an AI algorithm and established database, that algorithm will be able to gather business data from a variety of sources (i.e. your business website metrics, third-party digital analytics tools, etc.) in order to present a well-rounded image of your business through the lens of its performance data.
To take the advantages of automation one step further, AI algorithms also provide the potential to automatically present data sets in pre-established presentation formats. This capability allows businesses to develop templates or structures for data reports that can then be generated automatically at routine intervals. In other words, AI allows businesses to present data without the need for manual, time-consuming data collation.
Adding context to data with a human touch
Finally, although AI and machine learning capabilities allow data scientists to collect larger, more complex data sets by establishing dynamic algorithms, it’s important to note that AI still isn’t capable of analysing data with the nuance of the human mind. In other words, an artificial intelligence is less likely to be able to consider extenuating factors behind the ebbs and flows in data sets over the human being who designed the algorithm driving that data harvesting project, and the unique contexts within which that project exists.
Yes, there are industry leaders and innovators who do seek to incorporate algorithmic solutions for factoring in context, but even with their developments, context continues to be an evergreen concern for big data. Simply put, the ability to apply contexts to data sets isn’t something that can be easily achieved by developing a program. This is precisely why data analysts can never be replaced by the algorithms they build.
Whilst AI has undoubtedly enhanced the capabilities of data analysts, this technology must still be considered a resource or tool rather than a solution for the total automation of the data collection and analysis process. The rapid digital transformation of businesses and other factors contributing to rapid economic growth and evolution mean that data analysts are more vital than ever before for mapping industry developments as well as predicting the likelihood of growth barriers, the projected severity of these barriers, and how these barriers can be overcome.
For those looking to commence a career as a data analyst, you’ll find that the sky’s the limit, both with regards to which industries you operate within and the trajectory of your analytical career.
