“Information is the oil of the 21st century, and analytics is the combustion engine.” A quote by Peter Sondergaard (1965), senior vice president and global head of Research at Gartner, Inc.
These days businesses are generating heaps of data points at every instance, Generating insights, which will help them to diagnose business issues better, generate better and timely solutions, craft better plans of actions and achieve the deliverables and reach the organizational goal faster. Data Analytics is the combustion engine of businesses & will give us the velocity to reach their goal post.
Data analytics is the process of examining data sets to find trends and draw conclusions about the information they contain. The aim of analytics is to turn data into information and information into insights.
Data → Information → Insights = Better Decisions
There are many ways where data analytics is proved its usefulness for businesses to make critical decisions. It decides where the Organization will be in near future. It has found its way across many industries such as banking and financial institutions, insurance, technology, pharmaceuticals & healthcare, manufacturing, e-commerce & retail, various other professional services.
There are different types of Data Analytics projects such as:
Descriptive Analytics: What’s happening or happened
It describes what has happened or what is happening over a given period of time. For Eg: How are sales happening, how many visitors or page views in last week, comparison of marketing campaigns, Profit percentage year on year, etc.
Prescriptive Analytics: What can be done
It suggests a particular plan of action. A typical of IF THIS…THEN THAT kind of suggestions. For eg: If temperature in summer rises up to 40 Degree Celsius then launch complete range of ice cream products or If the inventory threshold reached at buffer level then release the fresh purchase order for particular products.
Diagnostics Analytics: This is why it happened
This shows more on why the event has happened. It involves many data inputs and generally which proves if the hypothesis is right or wrong. For Eg: Did the dengue outbreak affected pharma product sales or did the latest paid campaigns impacted the last month sales.
Predictive Analytics: This could happen next
It predicts what is likely to happen next. It usually takes the historic data, set of rules, modelling as an input and based on that predicts the likelihood of event happening in the near future. For Eg: sales trends for the seasonal products across regions, etc.
Using the combination of each can give a good sense of understanding and help in critical decision-making.
Each functional stream in every business and every function is generating heaps of data as per their operating process.
Data analytics can be applied in many areas such as:
- Customer acquisition and retention
- Customer segmentation & Targeted ads
- New product development and research
- Supply chain and distribution optimization
- Financial analysis
- Sales planning and forecasting
- Employee performance and reviews
- And many more
Data analytics can help businesses in almost every functional stream and gives many benefits such as:
- Data-based informed decision making
- Personalized customer experience based on past data
- Preventive & Corrective Measures
- Planning & Mitigating risks
Analytics success isn’t just about data collection, it’s about data management and insight. Here are one expert’s 10 steps for improving business decision-making. — MIT SLOAN
Top Data Analytics tools of 2021:
- R and Python
- Microsoft Excel and Spreadsheets
- Tableau
- Power BI
- QlickView
- Alteryx
- Looker
- DOMO
- SAP Business Objects
- Google Data Studio
- Oracle Analytics
- RapidMiner
- KNIME
- Apache Spark
- Talend
- Splunk
- Thoughspot
- IBM Cognos
- Metabase
- TIBCO Spotfire
- SAS Business Intelligence
- Redash
- Periscope Data
- ChartIO
- Sisense
- Mode
Get started with Data Analytics today.