Ramakant is a user experience (UX) designer specializing in interaction design (IxD), interested in human–computer interaction (HCI) and cognitive science, passionate about good design and creating products for multiple channels like handheld devices, PC, iTV etc. Currently at Clarice, he has designed consumer and enterprise applications for fortune 500 companies, which deals with big-data analytics and data visualizations.
Have you ever thought about following events –
- Why your telecom service provider is offering you the internet plan you thought about ?
- Why your Facebook social games are providing you exactly the same offer with required packs ?
- How does Netflix frequently recommend just the right movie?
- Why is it better to charge an electric vehicle overnight and not during the day?
- How do grocery cashiers know to hand you coupons you might actually use?
- Why do airline prices change every hour?
- Why does Facebook often find your acquaintance as potential friends?
It’s all possible because of the power of Analytics!
The root of the word is “analysis” or “analyze”. Technically, to analyze something is to break it into its constituent parts. A less formal definition is to examine something critically to understand its essence or identify causes and key factors. Information technology is going to do real things to make our lives better. The data is out there. Now it’s just a matter of maximizing the utilization of that data, organizing and analyzing it so that businesses can make efficient decisions.
Analytics help to build data driven insights for better decisions. Analytics can be viewed in three stages: Analyze-Act-Adapt. It’s essentially a scientific method: observe, define, measure, experiment, learn and act. It’s all about effecting better to make the invisible visible. It’s really about people; people who make better decisions to make better result. Analytics supports the same by unleashing it’s full potential.
Analytics helps people to collect and analyze data in form of information from single or multiple sources and use it to arrive at logical decisions. One of the most frequent uses of analytics is in the field of business for multiple domains like telecom, healthcare, information technology, banking, entertainment, media and many more.
In organizations, business analytics enables professionals to convert extensive data and statistical and quantitative analysis into powerful insights that can drive efficient decisions. Business analytics can answer essential questions – like why is a particular event happening, what if these trends continue, what will happen next and likewise-problems that form the crux of any decision in a company, based on hard data. Therefore with analytics organizations can now base their decisions and strategies on data rather than on gut feelings. Moreover with the rate at which this data can be analysed, organizations are able to keep tabs on the customer trends in near real-time. As a result effectiveness of a strategy can be determined almost immediately. Thus with powerful insights, analytics promises reduced costs and increased profits.
Analytics have mainly two dimensions: Purpose (analysis and reporting) and Timing (backward and forward-looking sometime real-time).
Levels of Analytics
Not all analytics are created equal. Like most software solutions, you’ll find a range of capabilities with analytics, from the simplest to the most advanced. In the spectrum shown here, your competitive advantage increases with the degree of intelligence.
1. Standard Reports
Answer the questions: What happened? When did it happen?
Example: Monthly or quarterly financial reports.
We all know about these. They’re generated on a regular basis and describe just “what happened” in a particular area. They’re useful to some extent, but not for making long-term decisions.
2. Ad-hoc Reports
Answer the questions: How many? How often? Where?
Example: Custom reports that describe the number of hospital patients for every diagnosis code for each day of the week.
At their best, ad hoc reports let you ask the questions and request a couple of custom reports to find the answers.
3. Query Drilldown (OLAP)
Answers the questions: Where exactly is the problem? How do I find the answers?
Example: Sort and explore data about different types of cell phone users and their calling behaviors.
Query drilldown allows for a little bit of discovery. OLAP lets you manipulate the data yourself to find out how many, what color and where.
Answer the questions: When should I react? What actions are needed now?
Example: Sales executives receive alerts when sales targets are falling behind.
With alerts, you can learn when you have a problem and be notified when something similar happens again in the future. Alerts can appear via e-mail, RSS feeds or as red dials on a scorecard or dashboard.
5. Statistical Analysis
Answers the questions: Why is this happening? What opportunities am I missing?
Example: Banks can discover why an increasing number of customers are refinancing their homes.
Here we can begin to run some complex analytics, like frequency models and regression analysis. We can begin to look at why things are happening using the stored data and then begin to answer questions based on the data.
Answers the questions: What if these trends continue? How much is needed? When will it be needed?
Example: Retailers can predict how demand for individual products will vary from store to store.
Forecasting is one of the hottest markets – and hottest analytical applications – right now. It applies everywhere. In particular, forecasting demand helps supply just enough inventory, so you don’t run out or have too much.
7. Predictive Modeling
Answers the questions: What will happen next? How will it affect my business?
Example: Hotels and casinos can predict which VIP customers will be more interested in particular vacation packages.
If you have 10 million customers and want to do a marketing campaign, who’s most likely to respond? How do you segment that group? And how do you determine who’s most likely to leave your organization? Predictive modeling provides the answers.
Answers the question: How do we do things better? What is the best decision for a complex problem?
Example: Given business priorities, resource constraints and available technology, determine the best way to optimize your IT platform to satisfy the needs of every user.
Optimization supports innovation. It takes your resources and needs into consideration and helps you find the best possible way to accomplish your goals.
Some Facts & Figures about Analytics
IBM’s recent study revealed that “83% of Business Leaders listed Business Analytics as the top priority in their business priority list.”
Deloitte has mentioned in its study that – Decision makers who can leverage everyday data & information into actionable insights for the growth of their organization by taking reliable decisions, will find themselves in a much better position to achieve strategic growth in their Career
“There is an information overload in today’s world and data analytics helps to cut out the clutter to help businesses make safe and smart choices,” said the Deloitte global MD
A recent report by Nucleus Research found that companies realize a return of USD10.66 for every dollar they invest in analytics. www.ibm.com
In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues. McKinsey Global Institute
A retailer using big data to the full has the potential to increase its operating margin by more than 60 percent. McKinsey Global Institute
All in all, Analytics is a one stop solution for you needs if you are looking to derive some sense out of vast repository of data in your possession.
I look forward to your comments…by