Businesses and organisations that continuously learn and adapt are the most successful. No matter what sector you work in, it’s critical to be aware of the recent past, the current state of affairs, and potential future developments. So how do businesses accomplish that?
Data analytics holds the solution. Most businesses constantly gather data, yet this data is meaningless in its unprocessed state. What you do with the data is what really matters. Data analytics is the act of examining raw data to identify patterns, trends, and insights that might provide valuable information about a certain business domain. Following that, informed judgments based on data are made using these insights.
AI and sophisticated analytics have gained popularity in recent years. There are a lot of blogs out there that discuss the benefits of employing sophisticated analytics in your company.
It is tempting to go right in and try to get advanced analytics straight immediately given the amount of value that they may provide. But these insights cannot be attained without the right foundations. What is the first step to obtaining these insightful data, then?
Advanced analytics success and the use of AI can be ensured by comprehending the analytics development and getting started in the proper spot.
56 percent of respondents claimed data analytics resulted in “faster, more effective decision-making” at their firms, according to MicroStrategy’s The Global State of Enterprise Analytics survey (pdf). Other advantages mentioned include:
- increased productivity and efficiency (64 percent)
- better financial results (51 percent)
- Finding and generating new sources of revenue for products and services (46 percent)
- enhanced client acquisition and retention (46 percent)
- enhanced client experiences (44 percent)
- advantage over rivals (43 percent)
Any business professional who takes judgments must have a solid understanding of data analytics. Data access is easier to come by than ever. You may overlook significant possibilities or warning signs if you design strategies and make decisions without taking the facts into account.
Skills in data analytics can be useful for the following professions:
Skills in data analytics can be useful for the following professions:
Marketers develop marketing plans by using information about customers, market trends, and the results of previous campaigns.
Product managers improve their companies’ goods by analysing market, industry, and user data.
Finance experts predict the financial trajectories of their organisations using historical performance data and market trends.
Human resources and diversity, equality, and inclusion specialists can use information on industry trends and employee perspectives, motivations, and behaviours to make significant organisational changes.
1. Descriptive Analytics
The foundation for all other types of analytics is descriptive analytics, which is the most basic type. It enables you to quickly summarise what occurred or is happening by drawing trends from the raw data.
What happened is answered by descriptive analytics.
Consider the scenario where you are studying the statistics for your business and discover that sales of one of your goods, a video game console, are increasing at a seasonal rate. Here, descriptive analytics can inform you, “Sales of this video game system increase each year in early December, early November, and October.”
Charts, graphs, and maps may clearly and understandably display data patterns, as well as dips and spikes, making data visualisation a good choice for expressing descriptive analysis.
2. Diagnostic Analytics
The following logical question, “Why did this happen?” is answered by diagnostic analytics.
This sort of analysis goes a step further by comparing current trends or movements, finding relationships between variables, and, when possible, establishing causal linkages.
Using the previous example, you might look at the demographics of video game console users and discover that they range in age from eight to 18 years old. The average age of the patrons, however, is between 35 and 55. Data from customer surveys that have been analysed show that buying a video game console as a present for kids is one of the main reasons people do so. The increase in sales throughout the fall and early winter may be attributed to the gift-giving holidays.
Using diagnostic analytics to identify
3. Predictive Analytics
In order to predict future trends or events or to provide a response to the question “What might happen in the future,” predictive analytics is utilised.
You can accurately estimate what the future may hold for your firm by examining historical data along with current industry trends.
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For instance, knowing that, over the previous ten years, sales of video game consoles have peaked in October, November, and the first few weeks of December each year gives you enough information to forecast that the same trend will continue in 2016. This is a logical prediction, supported by upward trends in the video game industry as a whole.
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Making forecasts about the future might assist your company in developing plans based on probable outcomes.
4. Prescriptive Analytics
Prescriptive analytics finally provides a response to the query, “What should we do next?”
Prescriptive analytics recommends actionable takeaways after considering all potential aspects in a circumstance. Making decisions based on data can be extremely helpful when using this kind of analytics.
To complete the video game illustration: What should your team decide to do in light of the anticipated seasonality trend brought on by the holiday gift-giving season? Perhaps you decide to do an A/B test with two adverts, one geared toward customers and the other towards the product’s end-users (children) (their parents). The results of that experiment can help determine how best to further capitalise on the seasonal rise and its purported cause. Or, perhaps you decide to step up your marketing initiatives in September with messages centred around the holidays to try to prolong the boost.