The shift in the way organizations work has been so noteworthy over the previous decade that individuals who have been doing business for no less than ten years could characterize their professions as BBD and ABD, or Before Big Data and After Big Data. The science of analytics has evolved to keep pace with the vast troves of available data.
Over the last several decades, companies have depended heavily on analytics to give them an upper hand and enable them to be more effective. Analytics has turned into an expected part of the bottom line and no longer provide the advantages that they once did. Currently, companies should look further into their data to discover new and creative approaches to build efficiency and competitiveness.
Evolution of Data Analytics
Although the ground rules for gathering and scrutinizing data still take shape, organizations know they should get in the game. They are collecting and mining data on clients, workers, market dynamics, the climate, and so on. As a result, they receive data using tools ranging from traditional business intelligence (BI) systems to more experimental ones. For example, geospatial and real-time mobile tracking technologies and social media analytics.
From around the late 1980s, the data gathered kept on increasing significantly, because of the ever-decreasing costs for hard-disk drives. That is when William H. Inmon proposed a “data warehouse”. In other words, the data warehouse is a system which helps in reporting data and analysis. The difference from usual relational databases is that data warehouses usually optimize for response time to queries.
Many times, less utilized data is put away with a timestamp and operations like DELETEs and UPDATEs. For instance, if a business needs to compare sales patterns every month, all business transactions can be put away with timestamps inside a data warehouse and questioned based on this timestamp. Essentially large organizations embraced it by analyzing client data systematically when settling on business decisions.
Data mining gained popularity around the 1990s, it is the computational procedure to find patterns in large data collections. By analyzing data differently in contrast to usual methods, the business can also expect beneficial outcomes. The development of data mining was made possible because of database and data warehouse technologies, which enable organizations to store more data and still analyze it reasonably. A general business trend emerged, where organizations began to “predict” clients’ potential needs based on analysis of historical purchasing patterns.
In the mid-2000s, Internet and social media giants like Google and Facebook uncover, gather and analyze a new type of data. In addition, this new information is qualitative and thus analysts can differentiate this from the small data of the past. Organization’s internal operations and transactions generate small data. However, this new data is external and firms can withdraw them from the Internet and public data sources. Therefore, businesses can imply the change to analytics 2.0.
With the arrival of big data, new technologies and procedures were created at warp speed to enable organizations to transform data into insight and profit. Big data required new processing systems like Hadoop and new databases such as NoSQL to store and manipulate it. The data analysts of Analytics 2.0 required competencies in both analytics and IT, which better prepares them for upcoming advancements.
Numerous experts believe that a third era — Analytics 3.0 — has arrived, evidenced by new customer-facing services that use analytics to give an exceptionally personalized customer experience. Currently, businesses use Revolutionary in-memory or in-database analytics and combine with responsive strategies and machine learning to deliver real-time results.
New analytics disciplines have emerged to complement descriptive analytics in the analytics portfolio. Predictive and prescriptive analytics, which offer insights to the probability an event will happen later on. Also, it will assist to prescribe possible courses of action, are emerging as crucial tools for business executives. Analytics are available to help on-the-spot decision making using analytical applications.
This new era of Analytics 3.0 is certainly not to reflect the end of the evolutionary tale. In fact, it displays new difficulties and opportunities for organizations and data analysts alike. Those people who can both capture data and organize it as well as analyze and use it to settle on better business choices are and will keep on being in demand.
Written By: Vic Bageria
CEO / CVO