The third phase of analytics is in vogue and how is it working for organizations
- As per a recent report from Gartner, over 70% of the organizations are taking steps toward shifting their focus from big to small and wide data.
Analyticshas empowered organizations to get insights that were previously hidden due to lack of advancement in technology. Data sciencehas emerged during the third phaseof analytics, paving the way for the need of data scientists.
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To keep up with its complexity organizations will have to start investing in the models that will help them extract the required insights from today’s complex marketplace. In fact, as per a recent study from Gartner, over 70% of organizations are planning to shift their focus from big to small and wide data.
While having a massive volume of data can help organizations get an edge over their competitors, not being able to harness it can put them at a great disadvantage. Hence, organizations have been utilizing analytics to effectively scour through the large volume of data.
Analytics has empowered organizations to gauge and gather insights that were previously hidden due to a lack of effective availability of tools in place. It has played a critical role in uplifting a few organizations to achieve new heights and strengthen their position in the marketplace.
However, the advanced analytics we are familiar with today hasn’t always been like that. There have been multiple phases in its evolution that have served the needs in the respective times while also pushing organizations to look for advanced analytics solutions.
The analytics evolution phases include:
This was the uprising of the data warehouse where customer (business) and production processes (transactions) were centralized into a huge repository such as a data warehouse. It marked the era where real progress was established to attain an objective and in-depth understanding of the critical business operations, allowing enterprise leaders to get fact-based comprehension while advancing beyond intuition decision-making.
The data within the data warehouse was captured, transformed, and queried with the help of ETL and BI tools. The kind of analytics taken during this phase was mostly qualified as Descriptive and Diagnostic. But this era had limitations. The potential capabilities associated with data were used within enterprises alone. This prevented organizations from predicting industry, effectively limiting their ability to advance.
This era has changed the world for the better. With the immense surge in data worldwide, in this phase of analytics the term ‘Big Data’ was coined. This phase has allowed organizations to deal with the shortcomings of phase one, where the analytics was not confined to large or multiple large entities. Enterprises irrespective of their sizes began to implement ways to deal with large sets of data and come up with meaningful insights.
Tools such as OLAP, data mining and more were invented during this phase. In this phase of analytics technology such as the internet proliferated, and has become more advanced, and mature and provided automated options for managing data. This allowed data analysts to analyze data, trends and more and come up with conclusions and recommendations like never before.
This phase of analytics witnessed the emergence of customer-facing products, services and features. Smartphones, Facebook (now Meta), Twitter, connected devices, etc marked their footprint in the digital landscape. Organizations in this space attracted users to their websites through better search algorithms, recommendations and suggestions all of which were deeply analytics-driven. This era also marked the creation of “Online Analytical Processing” platforms by big tech giants in the marketplace.
Knowing that enterprises will have to tackle the issues related to unstructured data, many firms familiarize themselves with a new class of databases known as NoSQL. New technologies were introduced for faster processing and machine learning models for advanced analytics. Data science also emerged during this era, paving the way for the need of data scientists.
While this phase of analytics evolution is still in its early stage, it is still able to gather data from hundreds of sources. We can see enterprises have already taken steps towards implementing advanced automated decision-making tools by using cloud and big data technologies, alongside predictive analytics. By incorporating leading cloud platforms, organizations are now in a better position to enable massive streaming and complex analytics.
The applications of analytics have evolved over the years from being confined to a single organization for manual analysis to the development of sophisticated platforms and algorithms. Given its journey so far, it won’t be surprising to expect the unexpected in the coming years as analytics technology advances.
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