Organizations are becoming more and more fast-paced every single day with the competition rising in each industry segment. With the advent of startups challenging the supremacy of big corporate, leaders are no longer making decisions based on gut feelings. Nowadays all executions and strategies need to have good data analytics for business. Moreover, accurate reasoning behind them supports some facts and figures. 

Data Analytics For Business

Analytics for decisions Insights relies upon datasets and to what extent they have sorted through for the greatest benefit of the company. Whether you are doing this via automation or sifting through terabytes of data. The goal is the same and that is to draw some conclusion based upon a set problem statement. According to McKinsey Global Institute, Data-driven institutes are 23 times more likely to get new customers and this is an astounding stat for companies of all sizes. This alone is sound reasoning for CEOs of companies of all sizes to use data to increase their revenues. 

Dataset Library

When it comes to utilizing datasets, businesses have the liberty of including historical data that is present within their systems or embarking on a new journey of collecting consumer information. This purpose is either particular initiatives or just to gather more information regarding their clientele.
 
This data may be available first-hand directly from consumers which might actually help the company interact more often with their customers or can be gathered or purchased from other organizations.
 
Any form of data that the company collects itself is first-party data. Whereas any information that is obtained from a secondary organization is second-party data. Either way, both forms offer the company datasets and information for analytical purposes.
 
The gathered data can range from basic information regarding consumer demographics to in-depth details about consumer interests, behavioral patterns, purchase trends, and so on.
 
At the end of the day, the focus is on gathering enough data to provide ample opportunity for analysis and study. In fact, most companies will look to the type of data that their competitors have been collecting and use that information to collect data on similar fronts to be at par with their competitors.
 
Sometimes, organizations might even collect competitor data to better sculpt their products and design trends that can better suit their consumer base. All in all, the onus of data collection is to gather enough information to provide organizations with the opportunity to learn more and grow exponentially over time.

Data Analytics Vs Business Analytics

We’ve understood data analytics as involving the analysis of datasets to learn about customer trends and insights.  To help the organization make better-informed decisions for the future.
 
Business analytics on the other hand rely on analyzing various types of information to make practical, data-driven business decisions and implementing changes based on those decisions. Business analytics will more often than not, use insights are drawn from data analysis to identify problems and find solutions to those problems.
 
So, even though the two might be on different ends of the spectrum. They’re still inter-connected in essence since both hope to understand behavioral patterns, study trends. Moreover, make informed organizational decisions based on what they’ve learned through the process of analysis.
 
However, this is not to say that the outcomes are the same for both – even though both of them work with data.
 
For business analysts, the focus is on using this data to help their organizations make more effective business decisions. Whereas for data analysts the emphasis is on simply gathering large amounts of data, analyzing it for businesses to evaluate, and then using it to make decisions on their own.

Data Analytics Technology

Before diving into the uses of data analytics in the arena of decision making. Let’s look at some of the technologies that make data analytics the force it is.

Machine learning

It is probably one of the most important components of data analytics – a subset of Artificial intelligence. ML enables applications to absorb data and analyze it in particular forms to predict outcomes without the need for any explicit programming into the system to achieve that conclusion. This means you only need a small subset to train the machine. Once that process is done, it can analyze large chunks of data without the need for any reinforcement.

Data Management

One of the important keys to the process since before you can move on to the process of analysis. You need to have a system in place to manage the flow of data and keep it organized. With the establishment of a data management program. Your organization will be on the same page regarding the process of organizing and handling datasets.

Predictive Analytics

This analytics technology sets the goals and targets for the future that is to come. With the usage of statistical algorithms and machine learning, predictive analytics provide organizations with the opportunity to make predictions that can impact business decisions in a manner that poses the business for future success. Through this, businesses can anticipate customer needs and concerns, predict future trends. Most importantly, they stay ahead of their competition!

Data Mining

Once data has been organized, the process of sorting through copious amounts of data takes place. Through this component, you’re able to identify patterns, discover relationships and sift through large datasets to figure out what is relevant and what isn’t. This “sifted” information is what organizations use to carry out a streamlined analysis that achieves the particular targets that they’ve set out for themselves.

Weighing the Benefits and Limitations of Data Mining

Data Analytics For Decision Making

With the understanding of data analytics and its various components. let’s now move on to the impact of data analytics in the arena of decision making and see how CEOs can use Big Data to their advantage regarding decision making.

Automated Assessments Of Teams

Sales and Marketing Teams can assess based on predefined criteria. The KPIs for these teams are generally the same provided the company has one core product. Based on these indicators, one can check the performance of individual team members. In similar terms, teams using the Agile model of working and having sprints with goals outlined could check using AI techniques. These assessments are impartial and can help managers make important decisions on shifting members from one department to another. Furthermore, giving bonuses or even firing some employees, etc.

Predictive Analysis For Goal Setting

An arduous task for a CEO is to outline quarterly and yearly goals. Importantly, to in line with the vision of the company. However, one has to define goals and tasks which are not ambitious but also not underwhelming to make sure that the teams are working to their full potential. These goals are the basis for the organization’s growth and revenue and should have greater consideration.

Big Data presents the opportunity of backing up each quarterly goal with a statistic supporting it. Additionally, outlining previous experience about why achieving the goal will help improve the company’s performance. Artificial Intelligence is capable of making these predictions for us. If, we have defined the scenarios and have a large enough dataset from which it can provide an outcome.

Learn How CXOs use Predictive Analytic for decision Making

Revenue Predictions Of Each Vertical Of The Company

For large organizations working on different projects. Big Data presents a massive opportunity to predict the success and failure of these projects. Each vertical is working on a different project, taking into account the company’s previous experience. Also, CEOs can make predictions on the forecast revenue of each division of the company.

The Importance Of Big Data In Today’s Enterprises

Conclusion:

These discuss metrics serve as a measure for performance evaluation of the division of the companies as a whole. For instance, Microsoft’s revenues these days are reliant on the cloud computing segment. Before, they scraped off the mobile division (Nokia) because it wasn’t keeping up with revenue expectations. However, this is one example of how data becomes a basis for decisions within a company. 

However, what matters is that organizations make the effort to understand their customers and their behavioral patterns. It is through the study of these factors that they can make better business decisions. Also, create products that can bring them success in the long run.

This is to say that no matter what, you can’t expect your business to grow or for you to outrun your competitor. If you’re not using the opportunities that data analytics is offering your business.