Data Science and Business Intelligence (BI) are two terms that are usually confusing to many. In fact, some of tend to use the terms interchangeably! Well, they are actually not the same. In simple terms, Business Intelligence activities entail creating graphs, charts and reports using data while Data Science is more about processes and systems that are deployed to extract various forms of data, structured or unstructured. Simple enough but that is still pretty ambiguous.
Although different, they two are equally important meaning that companies have to look for the best data science experts and BI analysts in a bid to gain a competitive business advantage. So, let’s see why do businesses need BI (Business Intelligence) and data science integrated.
Importance Of Data Science And Business Intelligence To An Enterprise:
Customer Relationship Management
There is no denying that modern customers have become smart enough and even social media has pushed competition little bit more. Nowadays, customers talk to their friends in social networks about their experiences thus the pressure is on businesses to deliver the experiences they crave. What better way to profile these customers than through data science and BI?
Customize Products and Services
With proper BI and Data Science functions, you can get a chance of perceiving your customer attitude towards your products and services. This way, you can provide best match product and services that will fit into the taste and preferences of the target demographics.
Risk Analysis
Business success does not only depend on the way a company is run, keeping in mind that social and economic factors do play a massive role as well. With BI and Data Science, you can get a proper look at the developments in your industry and the business environment both at present and in the future. This can be a great tool in assessing the risks when making certain decisions in your enterprise.
Protect Data
In a world where cyber attacks are a growing concern, data security is no longer a choice but a necessity. There is a need to map the entire landscape of your enterprise to analyze threats and this can be done using a combination of BI and Data Science techniques. You might end up finding some loopholes in the systems that will surely need to be sealed.
These benefits are just the tips of the iceberg, there are lots of benefits of Data Science and BI to businesses but let’s turn attention to the differences.
What Are The Core Differences Of Business Intelligence And Data Science?
Now, it is important to make this distinction especially when you are recruiting. For example, you do not expect a BI analyst to make proper business forecasts. This is just an illustration of the need to draw the line. The following part of this article dissects through the major differences between Data Science and Business Intelligence that will help in aspects like picking the right candidate for the enterprise for the right task among others.
Focus Areas
As stated earlier, BI entails using historic data to generate dashboards albeit according to a set of metrics agreed upon by a business. In other words, BI is reliant on current trends and reports. Data Science, on the other hand, will focus more on predictive analysis for the future. Talk of things like studying patterns and models coupled with finding correlations between various patterns. For example, a food outlet can predict new products to sell through existing patterns and demand.
Data Analysis Methods
Data Science adopts predictive and prescriptive analytic algorithms and techniques to deliver accurate predictions about the future. For example, it can be leveraged in estimating the revenue generated by a company over a period of time. Data Science relies on probabilities and confidence levels that cannot be 100% accurate for sure. As a matter of fact, companies are able to conjure up some necessary steps and measures based on the projections. This implies that the results of Data Science must be good enough to let an enterprise make timely decisions.
BI calls for analysts to look at historical data-some sort of retrospective analysis. BI actually demands top notch accuracy given that it is built on what happened in the past. For example, in case reports are generated using actual business revenue results, then they should be very descriptive and not judgmental.
Data Sources
To make good use of BI, companies have to dig dip in the quest to find the right data sources for proper data transformation. This calls for advanced planning and preparation. In data science, data transformations can be created by simply using available data sources.
The Need For Mitigation
Now, since Business Intelligence is based on actual occurrences, there is little or no need of mitigating uncertainties. Of course, this is based on the reasoning that historical data is not based on probabilities.
In Data Science, on the other hand, uncertainties have to be controlled if proper predictions are to be delivered. For this reason, data scientists adopt sophisticated analytic and visualization methods on the data. Proper transformation techniques are also used to convert data into palatable format-formats that allow the data to be merged with other data sources.
Process
Data transformation as far as BI is concerned tends to be a bit slow thus transformations can’t be done instantly. BI is thus considered a slow and manual process that calls for a lot of pre-planning. This means that it has to be repeated from time to time. For example, it can be done monthly, quarterly and annually.
As you might have guessed, data science tends to be faster. Perhaps what you didn’t know is that it calls for a lot of experimentation targeted at creating swift data transformations through predictive applications.
Conclusion
Now that we have made the distinctions, it’s time to make smart choices. Remember that both BI and Data Science are essential components if you wish to drive your business to the next level. The key is to make distinctions between the two if you are to integrate them well into your business!
