What Leaders should know about Advanced Data Analytics

One of the biggest things to shake up the world’s business landscape over the past decade is the field of advanced data analytics. But it’s not always easy for business leaders to understand and adopt data analytics into their business.

Over the past period we have spoken with business owners and managers in among others the real estate, recruitment, and online education sector. We discovered that for a lot of them the field of data analytics is not implemented yet or not fully exploited.

The main reasons were:

  • High-implementation costs
  • Return on investment uncertainty
  • Difficult to understand and oversee the business advantages

Let’s start with a closer look at what advanced data analytics is, why it matters for your business, and how you can optimize your business by using data analytics.

Data Analytics goes beyond Business Intelligence.

What is Advanced Data Analytics


Advanced Data Analytics goes beyond Business Intelligence (BI). It will look at all your stored raw data, technology landscape, external data sources; to discover deeper insights, make predictions, or generate recommendations possibly based on ‘what if’ scenarios.

Rather than answering questions about what happened, data analytics strives to learn why things happened.

For example, you might recognize this situation; where you as a manager get extracted information in the form of generated reports, excel sheets, or maybe visualized in a dashboard by a business intelligence software.

Your most crucial questions are not answered with this kind of basic analysis, providing you with information about what has happened in your company and not what decisions and actions are the best to improve its future.

You must do this yourself or throw it in the group discussion, to figure out what is most likely the best move to make. Once the decision has been taken you might find yourself in a period of uncertainty before you can confirm the results.

Advanced data analytics can show you the best directions.

The key is to not force complex analysis under the assumption that it will always lead to higher gains.

The three categories of analytics

Data can be analysed in different ways to draw meaningful insights that show the strong and weak areas of your business. It all depends on what we want to achieve.

We can split analytics into three categories: descriptive, predictive, and prescriptive.

  1. Descriptive analytics takes data and turns it into something business managers can visualize, understand, and interpret. It provides intelligence into historical performance and answers questions about what happened.
  1. Predictive analytics uses techniques to identify the likelihood of future outcomes based on historical data. It goes beyond knowing what has happened to provide a better estimate of what will happen in the future.

  2. Prescriptive analytics offers advice about what actions to take using advanced modelling techniques and knowledge of many analytic algorithms. It suggests which actions will have optimal outcomes.

Which one do you need? Most probably a combination of the three. However, the key is to not force complex analysis under the assumption that it will always lead to higher gains.

Why Data Analytics matters for your business

You’ve been collecting data for years, whether you know it or not, collected from customers, sales and operations.

By investing in data analytics and making it a priority in your organization, you’re already setting yourself ahead of competitors who don’t. And by using your data analytics, you can find new opportunities that your competitors won’t, allowing you to move tactically ahead of the competition.

Here are four key matters how data analytics can help your business:

  • Make your business more viable by outperforming the market
  • Identify new opportunities, markets, and market trends
  • Avoid costly problems by using predictive analytics on key decisions
  • Reduce costs by recognizing the correct efficiency advantages

There is no need to start with crazy huge projects. Though, it’s better to start the shift from yesterday’s logic to tomorrow’s logic, today.

Don't get trapped into costly data solutions.

Business case first


The biggest mistake many mid-sized and smaller companies make is to implement a data analytics solution without having defined their business case (s) first.

It happens all the time. Companies acquire a mammoth business intelligence software full of features that they do not need. Resulting in an overspend, unproportioned time consumption to learn and maintain the system, and to find out that extra modules need to be licenced to achieve their initial goal.

So, first define the business cases in detail, make an inventory of the ROI, implementation time, complexity, impact on the organization, before selecting any data solution.

Don’t get trapped into costly data solutions that is more a state of the art rather than an effective solution.

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Read also How to Reduce Risk and Initiate a Data Analytics Solution Successfully

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