Guidelines on Building Analytic Infrastructure
In tandem with social, mobile and cloud, advanced analytics and associated data technologies have been gaining traction as one of the core disruptors of the digital age. Today, enterprises are in need of a centralized analytic platform that provides flexible, multi-faceted analytic environment to gain a competitive edge and make better decisions. With the help of data science and business analytics, professional organizations can build a big data analytics infrastructure that drives better organizational decision-making and enhanced performance. While building analytic infrastructure, an analytic platform is often referred to but hardly architected. Often data scientists and business analysts talk about the power of analytics but fail to address the complete plan of deploying. However, in order to make a significant move into big data world and stay ahead of the game, enterprises must alter the way they think about analytics and build a better analytic infrastructure to experience the benefits of business analytics.
Initially, to build analytic infrastructure, enterprises used to follow application centric approach which resulted in usual problems like silo systems, inaccessible data and data quality issues. For this reason, most companies today incline toward data-driven approach so that every department gets an access to the information needed to make decisions based on data. In order to make this approach work enterprises must make analytics more scalable and sustainable.
For instance, instead of building a black box application, enterprises can develop a platform that provide access to all the information and then apply the analytic. By creating a well planned structure that answers the big data challenges and leverages multiple analytic techniques can help enterprises in reaping insights from the analyzed data.
Further, in order to get more insights from the analytics, enterprises need to utilize various unique analytic techniques. For instance, building applications with loosely coupled data can provide seamless access to analytics across various organizations.
Lastly, by identifying a way to provide self-service analytics for all the different skill levels, businesses can overcome hardwiring analytics into an application.
The major factor that drives these objectives is by making data reusable so that it can be accessed by manifold analytics processes at all times. But it is not always guaranteed that data will always go into a tightly controlled model, like a data warehouse. For this reason, enterprise must create a robust foundation so that when it is needed it can be made available instantly.
Earlier, firms used to over-integrate and over-model their data, which led to slash in the operational cost and an unchangeable architecture. Today, the times have changed, companies are facing opposite problem under-modeling and under-integrating increasing both cost and complexity. The best way to capitalize on the data is to invest in tightly coupled integration for high-value data that will be used at scale.
With the help of following approach organizations can build an efficient analytic infrastructure:
It is essential for companies to quickly address the new requirements. By avoiding the application centric silos, firms can reduce the cost and complexity of the infrastructure. Further, by spending more time on making analytics user-friendly would benefit many people and also increase productivity. On top of that, new data can be integrated at lower cost since time and money are not being wasted over-modeling data that will not be used. This new approach will definitely encourage enterprises to easily build analytic infrastructure. And working with an experienced advisor can help in bringing infrastructure to reality.
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