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April 23rd, 2013

Big Data Analytics: 5 Tips to Implement Analytics into your Business

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IF YOU WERE TO SEARCH FOR “BIG DATA” IN GOOGLE, you will get 928 million results. If you were to search for “Big Data Predictive Analytics,” you will only get 4 million results. Although these are both still large numbers, what this indirectly tells us is this: Even though there is a great deal of awareness on big data, it has not been effectively translated into how to gain insights from it. Applying analytics on big data is still a challenge faced by many organizations today. It is no surprise that the businesses who are smart about data mining and use analytics to drive decisions are able to engage more effectively with their customers and ultimately gain the advantage over their competition.

Today, data flows in all directions from social media, blogs, forums, smart phone applications, surveys, and other unstructured digital channels into a company’s ecosystem. The real benefit to a business using analytics will come with the understanding of the 360 degree view of the customers’ needs and wants who are omnipresent in multitude of these data sources in the form of big data.

In my personal experience with large firms, it is common for Senior Management to acknowledge that big data can provide meaningful and action-able insights that produce better performance. However, they tend to struggle with where and when to start leveraging this data for insights.

Leading businesses into institutionalizing analytics for their entire organization, I have found game-changing results for companies applying these 5 basic principles:

1. Define and Scope: Every typical business faces pain points and challenges like growth, retention, and costs. It is important for the senior leadership to understand the top priorities and then focus on few key areas where analytics can help to identify root causes of the business issues. Once the business issue that needs most attention is identified, you will then need to set a realistic goal of achieving measurable success for the next 6-12 months. The objective could be to reduce customer attrition by 2% or increase margins by 3%, or increase coupon redemption rate by 5%.

2. Identify the Business Sponsor: Once we identify business area and issues, it is important to identify a business sponsor who can communicate effectively with both Macro (Senior Leadership) and Micro (Technology / Analytical Audience) in the organization. This is the most critical factor where organizations seem to falter from organizational hierarchies and departments. The business sponsor should play a central role in controlling and communicating the findings for future analytical projects across the organization.

3. “Everything” or “Nothing” Mentality: Don’t wait until you have the perfect data warehouse to do analytics! It is important to take the first step in starting with a POC (Proof-of-Concept) project and measure the initial analytical results within 3-6 months. Several companies think that they should have all the data and attributes ready to start a analytics project, which is not correct. Learning and applying small concepts in the right direction based on the data is more powerful than using intuition backed execution until the data warehouse is ready.

4. Managing Speed vs. Accuracy: The last decade has been spent building big data warehouses to host all of this big data. However, organizations are yet to see any ROI coming out of implementing these monster data warehouses. The main reason for this is a typical large data warehouse implementation can take upwards of one year before any business user can see any viable report coming out of it. During this time, an organization can easily collect another billion data points, as well as facing any new challenges and issues. In order to be successful, and reduce the period from a business issue to a solution, it is better to take raw data from a specific focused business area and then apply analytics to that one area. The key is to pull actionable insights from the data to understand “Why” and “What Next” in order to shortcut this time to weeks rather than months. For example, in one of my projects, we found 30 out of 500,000 customers who were at a high risk of leaving. This loss would cost an estimate of $4 million dollars for the organization. This revealing insight helped the organization to avoid a huge revenue loss within 2 months rather than waiting for a completed data warehouse implementation. In the 18 months engagement with this client, we completed multiple analytical projects converting raw data from the data warehouse into intelligent data providing insights. The below diagram explains how a data warehouse implementation (top section) and analytical development (bottom section) can work in parallel and simultaneously provide a real-time flow of intelligent data from analytics into data warehouse.
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5. Data Visualization is the Key: It is typical for an analyst who has been working on a project for more than two months to show all the frequency or statistical results with a presentation deck consisting of hundreds of slides. A few charts with great data visualization is worth a thousand slides. Actionable visualizations like Price or Attrition Alerts can help sales teams to better engage with their customers instead of analyzing a plethora of reports. The key is that reports should be easy to understand as well as recommend the next actionable step for business leaders. The truth is, smart organizations (small, medium, and large) are already working on leveraging big data and using it to their advantage. The time is now. Don’t be left behind in this race, your competition may be already in the game.

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Tags: big data insights

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