What’s the Big Deal of Big Data?

With all the buzz of Big Data and the million’s of dollars being poured into corporate efforts, are we doing it right? In a few weeks, I’m sure there will be plenty of Big Data discussion at MIT’s Sloan CIO Symposium.  It should be reviewed across all C Suites.

What will it bring to business?

Why are we still waiting to get it?

Where should Big Data be positioned within the enterprise framework?

Is it a fad?

Is it really new, or just a new name to an old concept?

As I talk to corporate leaders and visionaries, there is lots of discussion around Big Data and mysteriously depend upon it for solutions, yet many have no idea what it is. With more and more automation of processes and everything electronically capturing data; we obviously create more and more data. It’s captured, and sometimes stored. We’ve been doing that for years. Can businesses implement and leverage Big Data on their own using open source, or do they hire professional services from giant software solution providers? So, what’s the difference now? Has it gotten bigger? Yes! I guess that’s why they call it Big Data.

The issue is value. Any business investing in Big Data plans, or strategies, better keep the value proposition in the forefront. It’s simple. What is it doing for the business? Is the business staying up with the increasing opportunities? The Big Data plan must hit at least one of these:

  1. Reduce cost
  2. Increase revenues or
  3. Improve the customer experience.

If not, the plan will ultimately fail.

Before the values, the business must get the data in order and manageable. It must identify ALL primary data sources, and make all other sources of the same data become dependent to that primary source. If this is not completed early, the business will cycle and cycle through repeated data clean efforts.   There are several methods to explore this data clean up, but it must be done early, or it can destroy your efforts to produce value. This is especially important for companies with a lot of legacy systems.  What data do you keep and what do you purge, and are you making use of the data you are paying to store?  According to a report published by the International Data Corporation (IDC) in 2013, only 1% of the digital universe was being analyzed. This must also enable clear API development and simplified IT architectural direction.

The business must also address how they are managing the data, along with its security and privacy. I will only briefly talk about this has I will go into this in more detail in later articles. The same concepts that apply to tangible assets and inventory management should also apply to data assets; it is what propelled Wal-Mart’s and Amazon’s Big Data success.   It is a strategic asset. The business must also remember that not all insights are good insights, and just because you have access to the data, it doesn’t mean it can be used without consideration to customers’ privacy expectations, contractual agreements, government regulations or cross-border data transfer laws and common sense.

1. Identify the Cost Saving initiatives that will support the Big Data Plan. I believe you should start with cost, because this can bring the most immediate returns to support your plan. Identify, the top 5 primary cycle cost drivers in your businesses process. Identify the time, systems, fall out, and non value customer interactions that produce waste in your business. Over identify ever element of data that is produced and determine if you can eliminate reproduction or replication. If sales processing or service create non value cost to the business, explore how data can become a predictor to service or process eliminator to sales.

2. Identify the Revenue Producing initiatives that can improve sales success by better targeting or profile pricing that become predictors of success. If you are building upon your base of customer’s, what are the characteristics you should be using to market particular products with the greatest success and the least cost. If you are retail, or your assigning territories, do you know the matching psychographic characteristic that match your sales person or retail image to maximize return? Do you understand margin variances to these demographics?

3. Identify data elements that can protect and enhance the Service Experience of your customers.  This element probably will also enhance revenues (ARPU, Win Rate, Churn) and Cost (Cycle Time, Churn, Service Reporting).  Some of the basic elements of how Big Data can help in the service area are related to tearing down silo’s and information between various departments.  How often does the fact that the left hand is oblivious to what the right hand is doing create a terrible customer experience, i.e.,  the order and the shipment of the product is wrong?

These 3 elements to Big Data have been in existence for decades, but were called data warehousing, data surveys, data queries. With data sources becoming larger, we cant forget the basic opportunities of the effort simply providing value; COST, REVENUE or SERVICE.  If you’re not capitalizing on these increasing opportunities, you should be worried that your competitors are.   With data sources becoming bigger, and probably not part of your core business, most businesses should look into outsourcing these enablements.

By Tim LaFaver, COO/Co-Founder Globulus

Tim LaFaver is Co-Founder of Globulus, a consulting firm with a team of Director and above talent, who each have more that 20+ years experience in their given field and industry. They bring “been in your shoes” experience to businesses.  He is a passionate Customer Advocate and Change Agent for Businesses.  Prior to founding Globulus, LaFaver was Assistant Vice President at AT&T where he led the creation of Customer Experience Team, Program Office for Fiber/Ethernet Build to over 50K Cell Sites, US Telecom Expansion in 30 Cities and International Expansion in Mexico.

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