Advanced Analytics: 7 Steps To Built Data-Driven EnterpriseAli Kidwai
What differentiates enterprises in today’s highly competitive markets is their ability to make accurate, timely, and effective decisions in all aspects to address their customers’ preferences and priorities. Enterprises across the globe have started using advanced analytics to analyze their data by combining information on past circumstances, present events, and projected future outcomes. By incorporating it into their daily operations, these companies gain control over the decisions they make daily so that they can successfully meet their business goals.
The advanced data analytics allow companies to have a “360 degrees” view of their operations and customers. The insight that they gain from such analysis is then used to direct, optimize, and automate their decision making with a wide range of analytics tools and techniques such as data/text mining, machine learning, pattern matching, visualization, forecasting, semantic analysis, sentiment analysis, network, and cluster analysis, multivariate statistics, graph analysis, complex event processing, and neural networks.
Why Enterprises Invest In Advanced Analytics?
In the present scenario, advanced data analytics has gone typically beyond traditional business intelligence (BI), to discover deeper insights, make predictions, generate recommendations, and highlight risks.
Let’s have a look at some of the real reasons that encourage the executive professionals to invest in Analytics. So, most companies decide to invest in it to:
- Enhance employee satisfaction & retention rate
- Advance the standard of products/services & performance
- Help create new revenue streams for standing products or services
- Boost customer satisfaction and retention rate
- Expand the customer base in existing markets & Insights to enter new markets
- Improve insights to customer behaviors
- Increase profit margins
How Can You Build A Data-Driven Enterprise?
Today, technologies such as mobile, cloud, and the Internet Of Things are creating humongous amounts of structured and unstructured data—but many organizations are held back by data silos. Building a data-driven business depends on developing analytics competencies that can convert data into valuable information to drive real-time decision-making.
So, putting advanced analytics at the front, let’s outline seven steps to becoming a data-driven enterprise.
1. Understand where you are on the journey. Ask questions to distinguish your organization’s current level of analytics maturity, and work with IT to uncover what data is already available to analyze.
2. Understand your business drivers, because, without a business goal, advanced analytics is useless. Research how other organizations are using analytics for ideas on possible use cases and consider how you can best use your data to support the business.
3. Create a data-centric foundation of innovation and insight. Match up possible use cases with existing capabilities to deliver quick results and secure executive buy-in. Work with IT teams to create a realistic roadmap for future projects and the IT investments they need to ensure close collaboration to lead from the top.
4. With the technology team, formulate the strategies for analytics, governance and data management. Also, ensure that people in different departments are collaborating and crucial information is not locked in organization siloes. Therefore, departments should share information to skyrocket their analytics efforts.
5. To build a data-driven enterprise, you need to have a robust organizational framework in place to experience fast analysis, data collection, processing, and consumption of dashboards and reports. There should be a strong process in place to manage the change because it defines which data sets to collect, manage, and build governance around.
6. A center of excellence can be set up in-house that establishes and inculcates best analytics practices, as it works as a forum for team members to share techniques and ideas.
7. Organizations should adopt a well-organized procedure to manage the conflicts and priorities which will result in the right processes in place, increased adoption, and reduced risk to achieve maximum business value with complete agility and flexibility.
Today, investing in advanced data analytics has become a rising trend that is expected to go further up in the future. This is because of the new dimension that is being added to it and with the involvement of advancing technologies like artificial intelligence, predictive modeling, machine learning, etc. So, with the help of these technology advancements, any organization can easily re-establish its business success.