Data science and Business Intelligence (BI) are two similar, but slightly different things. It’s important to understand both the similarities and the differences. Both data science and BI involve using data in order to help businesses perform better. Both require analysts or data scientists to interpret the information. The difference is that BI requires structured and static data. Data science can work on larger, more complicated sets of data that come from a variety of sources.
To be clear, both are valuable. Getting the most out of BI and data science requires staying on top of the latest developments and making use of emerging technologies. These are evolving fields and keeping up can be challenging. If you plan on incorporating BI or data science into your business, here are five things you should know.
More Self-Service BI Solutions Will Emerge
BI was once largely a function of IT departments that would then interpret the data and provide information to other business areas. Gartner predicted that by 2017 this would shift with those business areas demanding more direct access. In 2018 and beyond, this will continue to come to fruition.
Thanks to self-service BI interfaces or ‘dashboards,’ users will be able to gain access to data, select what data they need, and how they would like the data delivered to them. As a result, they will see improvements in the usefulness of that data. “IBM has created Cognos for the purpose of providing non IT users with the tools needed to access and analyze data for business analysis and prediction” – explains Marie Fincher, a content manager at Trust My Paper and IBM contributor.
As self-service becomes more popular, the technology will shift to meet the demands of every day users. Developers will focus on solutions that are mobile friendly for example. There will also be an increase in cloud based BI solutions.
Blockchain Can Combine With Big Data to Increase Information Integrity
It’s not enough to have plenty of data to analyze. Analysts and business users must be able to trust that information is accurate. In areas like health care, fintech, and the government this is extraordinarily important. When blockchain technology is implemented to handle data, access to data, changes to data, and ownership changes are tracked making it much harder for hackers to impact data integrity.
At the same time, data science can be used to further improve the security of blockchain data and systems. It can be used to analyze and predict where it is most likely that security breaches in data sets can occur.
R And Python Will Continue to be The Prevailing Languages For Data Scientists
53% of data scientists use R or Python in their daily work. Anyone interested in entering this field in the next 12 months or so should probably make learning one or both of these a priority. Python in particular is likely to grow in popularity. It’s a user friendly language that is relatively easy for people without strong tech backgrounds to pick up.
SQL follows behind closely in popularity. After that comes C, Java, and Matlab.
Visual Analytics Will Become More Popular
As data shifts out of IT into other business areas, the demand for visual analytics will increase. Rather than poring through pages of reports and raw data, decision makers want information presented to them in a way that they can quickly understand and act. This is accomplished through charts and graphs, not huge tables.
The most popular dashboards will also allow users to quickly drill down into the information that they need. Software companies in this niche that wish to remain viable in the future will continue to develop sorting, filtering, and other capabilities that make data analytics and BI tools more user friendly.
Ethics Will be a Point of Concern For Everyone
Data science and BI both involve the collection and storage of data, often for uses beyond their original purpose. This brings up ethical concerns regarding privacy, data integrity, automation, and the potential for AI and data create potentially intelligent personas.
In the past, many of these concerns remained at a more academic than private level. This is quickly changing. Further, people are becoming more aware and wary of their personal information and how it’s being used. In some cases, governments are responding with regulatory changes such as GDPR.
It’s important that conversations about data and ethics continue. Data scientists and BI analysts have an important role to play in these conversations as they are most qualified to ensure that these discussions are based in fact, not rumors or suppositions.
Conclusion: Data Science and BI for All
Probably the biggest trend here is the mainstreaming of data science. Using data to make predictions, analyze trends, or simply gain a better understanding of a business is no longer just a function of academia and well-funded corporations. These technologies and tools are becoming more and more accessible to smaller and emerging businesses.