In our daily morning routine post, we defined data analytics as:
“the application of a sequence of steps (algorithms) or transformations to generate insights from processed datasets.”
Broken down, analytics consists of collecting, processing, analysing and interpreting data to derive insights. We can collect data from many different sources – from our gardens to our sport teams. We can process it in a myriad of ways. We can define our questions and work with data to answer them. And finally, our insights can have direct, measurable impact on business today.
DEMYSTIFYING BUSINESS ANALYTICS
In business parlance, analytics has gained a star status, finding application across industries. The exact definition and identification of the word changes between industries and verticals. For some, analytics could refer to the analysis of traffic or the performance of a website or app. For someone else, it could be the effort to derive insights that can improve sales or the supply chain. It varies according to its application, but the process remains the same.
Irrespective of the usage, analytics refers to the statistical and numerical analysis of data. This analysis results in insights that businesses can use to perform better. This concept of analytics has become mainstream and is popular across industries.
MOUNTAINS OF DATA
The volume of data generated every day is increasing.
From wearables, GPS, smart home security systems, Wi-Fi enabled microwave oven and fridges to smartphones and computing machines, the number of devices generating data has been extensively multiplied with the advancement in technology. Our online social activities generate data too – from browser logs to cookies and traces. We sign up for newsletters, and ‘like’ articles. We search, buy, tweet, heart, pin, chat, favourite, share and email stuff to our acquaintances.
In the corporate sector, there are many departments that generate and analyse data, including HR, Admin, Marketing, Sales and Finance. The volume of data they produce is growing as our businesses become more complex.
As more technologies emerge, so do potential sources of data. Personal analytics, sensor networks and advanced technologies will produce more data than ever before.
All this data production has no value, if the data produced can’t be analysed.
THE STATE OF ANALYTICS TODAY
Our ability to manage and analyse data has kept pace with the amount of data we generate. With advances in quantum computing and storage technology, analytics is growing ever more powerful. We can process thousands of data manipulation instructions in a few milliseconds. With today’s supercomputers, we can handle massive datasets many gigabytes and petabytes in size, in real time.