By Ciklum, September 27, 2017, 4:19 PM
The emergence of big data has started to shape the way companies understand clients and market to prospects.
Even though businesses and consumers are creating, using and interpreting more data every day, the difference between business analytics and data science may not be completely clear.
Business analytics allows companies to evaluate collected data, and then use it to provide actionable insights for sales, marketing, and product development teams, just to name a few. These analytics can be broken into three categories: descriptive, which tracks performance, predictive, which uses past trends to predict the likelihood of future outcomes, and prescriptive, those that create recommendations from past performance.
By understanding not only how clients and prospects interact with their business, but also how the business itself operates, companies can begin the process of automating and optimizing their workflow. If all of a company’s analytics are run off of the same platform, this is a simple process. However, this is often not the case. When data is pulled from multiple social media platforms, public website analytics, newsletter opens and a CRM, such as Salesforce, each platform’s exported data will likely look different. This is where data science begins to come into play.
Data science sounds like something that would be done in a laboratory by people wearing white coats. However, data science is more closely related to mathematics and computer science than physics or geology. In a business sense, data science is responsible for taking the data mined from the sources mentioned previously. Then, the data is mined into one database via a series of automated actions and algorithms so that actionable insights can be determined. Without data science, business analytics wouldn’t be possible.
As a discipline, though, data science is far more complex. This method of running automated tasks and algorithms to create digestible data is helping to create breakthroughs in healthcare, science, city planning and even the humanities. Through this field, data itself is creating more data thanks to the insights that can be gained. Knowing how, when and why to perform these tasks is extremely difficult work, which explains why data scientist jobs were ranked the number one best job to have in 2016 by Glassdoor.
While in the past, analytics may have been “nice to have,” it has quickly become a “must” for many businesses. According to a 2016 IDG Enterprise study, 78% of enterprises stated that collection and analysis of data has the potential to completely change the way their company does business.
Unless big data is analyzed and used, there is a fear that those who do not adapt will be left behind. The data is available, it’s now a matter of knowing how to use it. For more information on how you can use data science and analytics in your line of business, contact Ciklum today!
How will machine learning impact data analytics?
What Are The Most Popular Languages For Data Science?
Big Data in 2017: 10 Predictions Everyone Should Read