For this article, we will be using the term business intelligence and data analytics interchangeably. Stitch Data has a great explanation of the differences between the two, but as a primer, data analytics requires a greater mathematical skill set and requires more training since it uses larger data sets and algorithms to make predictive analysis. In contrast, business intelligence uses programs like Tableau and Microsoft Power BI and typically tries to understand what happened in the past. Additionally, business intelligence is focused on what happened—data analytics asks why.
In a way, consider business intelligence data-analytics-lite, diet coke to data analytics classic. Some of Google’s platforms are also evolving to follow the emphasis on business intelligence. Google Analytics was originally more focused on data reporting, but now the new GA4 is more focused on data visualizations and business analytics.
1. Increased adoption of Business Intelligence and Data Analytics in industries
Data analytics and business intelligence is here to stay and it’s only getting bigger. According to Dresner’s 2020 Cloud Computing & Business Intelligence Market Report, a whopping 95% of enterprise software vendors believe data analytics and business intelligence are a must-have for their business. More than half of the respondents said that the two were critical for their businesses.
Now when we say data analytics and business intelligence are crucial for businesses, we don’t mean they’re only crucial for businesses with strong digital footprints, like app developers or Amazon sellers. We predict that all businesses, ranging from manufacturing to clothing and trucking companies, will be using some form of data analytics, be it analyzing customer trends or simply using analytics to better understand how to build a better environment for their employees. This in turn leads to our next trend.
2. Bigger Automation
There is too much data. For those of us familiar with the data world, this isn’t a new phenomenon. Way, way back in 2014, while data analytics and business intelligence were still in their relatively formative stage, The New York Times wrote about how big data collectors were mired in data “janitorial work.”
Lots of data might seem to be a good thing, but there is always too much of a good thing. With so much data available it becomes harder and harder for good data analytics and business intelligence professionals to manage data, and sort through the relevant bits and pieces to make strong predictive analyses. There isn’t an easy way to deal with this problem, but there are solutions on the horizon.
More and more basic data is being automated thanks to processes like automated machine learning and embedded analytics; this frees up more time for the data professional to understand the root causes and find the pivotal data that can lead companies in the right direction. This transition is already happening as Adobe’s 2020 Digital Trend Report proved: 64% of large organizations said they used AI to automate data analysis in 2020, up from 55% in 2019.
As Suresh Vittal, chief product officer of Alteryx, a leader in analytic automation, told Forbes: “Automation is essential because, first of all, it frees up the analyst to focus on the high value-add activities, which really drive top-line growth. Secondly, it helps contribute to the bottom line by trading out mundane activities for more efficient processes.”
Or as consultant and thought-leader Pritam Gurumayum put it memorably: “Contrary to its name, artificial intelligence helps humanize data and make it more accessible to all.”
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3. Growth in Data Literacy
As data analytics becomes more prevalent, the need for every employee to understand data basics becomes paramount. More and more employers already ask jobseekers about the basics of data sets and how to understand them. This is a natural reaction to market forces. According to the Data Literacy Index large enterprises that have higher corporate data literacy experience $320-$534 million in higher enterprise value (the total market value of the business).
Consequently, this also increases the need for data analysts, data scientists, and other professionals who can adequately explain how data works to their counterparts. Data professionals are beginning to understand their new role in large companies as not only how to learn from data analytics but how to teach others about data analytics.
4. Stronger Reliance on Multiple Cloud and Hybrid Storage and Service
As more and more companies, both large and small, migrate their data to the cloud and use cloud-based software, we are seeing a growing trend in the use of multi-cloud storage and hybrid systems. This is a natural evolution in the growth of cloud computing. For many companies, a single dedicated cloud platform or infrastructure is no longer enough, and we expect companies to actively seek out multiple vendors for their cloud computing needs.
Using a multi-cloud system– where cloud services are provided by different vendors– or a hybrid system –where cloud services are used alongside legacy software, private clouds and on-premises software and hardware– allows companies to optimize their performances with better scalability and flexibility, while cutting down costs. Using multiple vendors also allows companies to take advantage of the best each cloud vendor has to offer, while avoiding locking in with one particular vendor.
As Ben Gitenstein, VP of product at Qumulo, an unstructured data management platform, told the Datamation site “Cloud solutions are now the name of the game, particularly hybrid cloud solutions for workloads that demand multiple storage environments,” said Gitenstein. “And as data continues to inevitably grow, enterprises require the flexibility and scalability only cloud services currently provide.”
5. Ethical Implications of Data Collection
Whether it’s Apple phones blocking location services in apps, a Facebook data breach, or an academic book about surveillance capitalism topping the best-seller list, data—the why and how it’s collected—has become a sort of ethical minefield for data professionals. This isn’t a bad thing; it’s a sign of how common and prevalent data analytics has become, and it also symbolizes how the profession is moving forward. No longer is data analytics and business intelligence relegated to the back-end of the corporate hierarchy; data analytics is front-and-center for many businesses. Having clear and ethical guidelines about data policy will save companies headaches later on.
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