What is Data Analytics
“Every business is a data business.”
Heidi Custers, Digital Transformation Strategy Manager, Deloitte.
In the foreseeable future, we are likely to see a dividing line between those who use data analytics optimally and those who don’t. Those who do, will have a competitive advantage over those who don’t, and with in-depth data analysis available with almost every software platform, if your company is not leveraging this gold mine of insights, it is handicapping itself.
How much data is there?
It’s hard to get our heads around the sheer volume of data that is processed globally every day. We can try by comparing it to the number of stars in our galaxy, which has about 400 billion stars, or 400 with 9 zeros following.
- 2.5 quintillion bytes of data are created every day: 2,5 with 18 zeros following.
- 90% of the total amount of all data ever made on Earth was produced by humans in the last two years alone. (Forbes)
That is a lot of data to wade through. This is why we now have “data detectives” because without an understanding of what the data can do for you it is just meaningless information.
What is a “data detective”?
Think of a data detective as a sleuth who delves into the data slurry and brings out the gems. They are puzzle solvers, who can find hidden messages and patterns in apparently jumbled reams of data. A data detective is like a data analyst but one who operates at a more profound level. They use the data analytics tools that are now available but have the intuition to see beyond the graphs and reports produced.
“They can interpret the data for deeper analysis and predictive modelling,” says Clinton Deavall of Dinkum Analytics.
Or as Beth McGuinness, Data and Analytics lead at IQ Business says, organisations “…need people who can speak the language of data and make it useful.” (Brainstorm Magazine, March 2020)
What is the difference between “Big Data” and “Data Analytics”?
The main difference between Big Data and standard data analytics is volume and scale. Data analytics is more focussed on a smaller set of data for a specific purpose, whereas Big Data starts out in a large, messy and unstructured soup that needs sophisticated processes to sort it, including AI and machine learning.
Data analytics has been around for a long time as businesses have always worked with performance statistics to make business decisions. The difference now is the increased volume of data that is available thanks to more advanced applications.
But how big is the “big” in Big Data? “Big” is immense and diverse: Facebook user stats would be called “big”; Google search volumes would be called “big”.
Data analytics seeks to give insight into a business with the data collected and every business has data that can be turned into useful reports for strategic decision making. This data comes from various sources, e.g. online and offline sources, IoT, databases, and more.
The analytics software exposes insights, trends, cause-and-effect relationships, etc. and analytics reports can be produced quickly – a lot faster than older models of DA, and ridiculously faster than manual analysis.
Why do we need data analytics – what are the benefits of data analytics?
The bottom line is, without analysis, data is just data. As Daniel Newman says in "Top 10 Digital Transformation Trends For 2020"(Forbes) -
“Companies that still aren’t investing heavily in analytics by 2020 probably won’t be in business in 2021. There is simply far too much valuable customer data to be collected, processed and turned into insights for any company to remain competitive without making full use of modern analytics tools.”
Data analytics saves money and promotes better business management:
- Data Analytics gives you a clearer understanding of how your business is performing.
- DA reveals where your revenue is coming from.
- It maps out what's working and what's not.
- It shows the level of customer satisfaction.
- It helps you formulate predictions with predictive analysis.
- It interprets patterns in the data to inform your decisions.
And countless other statistics will help you make crucial business decisions.
(See: Top 5 Benefits of Integrated Reporting Tools) With this knowledge, you can eliminate the “dead weight’ and make better plans. Here are just two stats that support this argument:
Data analytics tools and AI/machine learning
To make data analytics work, you need the right tools, i.e. the right software solutions.
Nowadays, it is almost a given that business management software will come with some sort of built-in reporting functionality. Organisations investing in this kind of software need to ask questions like:
Does the software platform incorporate reporting, charts and dashboards that are configurable to the specific requirements of a company?
Does it integrate with the other systems to provide a wholistic view?
Can the reports drill-down to the underlying data?
There are also specialised software solutions on the market that focus specifically on data analytics, but each business needs to assess its own needs.
Artificial Intelligence and Machine Learning
How do you wade your way through heaps of metrics without it becoming overwhelming and non-sensical? You need some sort of process to analyse the raw data, especially if your business generates a lot of it. AI, coupled with machine learning, takes the tediousness out of data processing and enables predictive forecasting.
As the algorithms become more reliable, AI and machine learning will become more sought after and useful. However, just what can be termed as “AI” is still debatable and we are still in the toddler stage.
Digital transformation is a commendable goal but as Willie Ackerman of 4Sight, the digital transformation agency, says (Brainstorm Magazine, March 2020):
“It comes down to data – if you haven’t got the data, digital means nothing.”
By Jeannie De Vynck