What better way is there to learn about big data than listening to a podcast about it? I found a really great podcast recently that dives into all of the different aspects and current issues that we are dealing with when it comes to big data. There are also podcasts that help us understand the best ways to save and make money with your data.
Their most recent podcast, 5 Lessons About Big Data brought some interesting facts to light. These 5 lessons can help you to leverage the data that you already have, and ultimately, save you money:
Use big data to make information transparent.
Create and store more transactional data in digital form.
Big data allows for the segmentation of customers so that companies can better tailor products and services.
Sophisticated analytics improve decision making, minimize risks, and ascertain valuable insights that would otherwise remain hidden.
Big data can be used to develop the next generation of products and services.
The next step is for you to use all of these lessons to add value to your data and your company.
Many organizations today struggle to get their cards in order and turn data into dollars.
“The more data you have, the more crucial it is to better manage your master data and improve the maturity of your master data management (MDM) program,” said Saul Judah, research director at Gartner. “Existing approaches to data management are, in many cases, insufficient to accommodate big data sources on an enterprise scale. Collecting data without managing it properly also creates ongoing costs as well as regulatory and compliance risks.”
In order to save money, CIOs and Chief Data Officers who oversee big data initiatives need to consider the following steps:
Update Information Strategy and Architecture
Many organizations have had success leveraging big data insight around specific business operations, but typically it’s limited to a single business unit or use case. Few firms have explored how to make big data insights actionable across the entire organization, by linking big data sources with trusted master data.
For example, many marketing organizations use data from social sources — such as Twitter and Facebook — to inform their campaigns, but they don’t reconcile this with trusted data in customer/prospect repositories that are used by customer services or sales. This can lead to incoherent customer communication that can actually undermine the sales or customer service process.
Become More Agile
Effective use of big data requires a mixture of old and new technologies and practices. This necessitates an agile approach that applies a bimodal IT framework to information governance (see “Why Digital Business Needs Bimodal IT”). MDM traditionally uses a Mode 1 approach which is policy-driven and approval-based. Big data typically uses a Mode 2 approach with little or no predefined processes or controls. Tactical and exploratory initiatives are much better suited to the “faster” Mode 2.
Move to Limit Risk Exposure
When an organization executes actions based on information sources outside the curation of MDM — as is the case in many big data implementations — exposure to certain types of business risk increases. Factors such as poor data quality, loss of critical information, and access to unauthorized information become more likely. Gartner recommends appointing a lead information steward role in relevant business units to assist in creating and executing risk controls with regards to data use in business operations.
All of the above steps to help manage your data can quickly turn around and save or make your firm money. You have the data- now just unlock the value of it with master data management.
Really when we think about what businesses are trying to do if we strip back all the mission statements and values etc., kind of what they’re there for is to either make money or save money. They tend to be pressured to do one or the other or in some cases, both depending on where the business is in its marketplace and its competitive construct, etc.
This is where business stakeholders will often initiate ideas that lead to data science because they’re looking for answers that they can’t find at the moment. They can’t find it intuitively, they don’t have enough data to find it or maybe they have sufficient data or they suspect they may have sufficient data, but they can’t find the insights or the answers that they’re looking for.
Two real examples of what was done in this space. They both actually are retail examples and retail is good because we all understand how retail works, at least at a superficial level once you dig beneath the surface, there are nuances we could never imagine. One of the big problems around retail is a little thing called shrinkage.
Now I’ll avoid the obvious shrinkage jokes and simply say that shrinkage is the term used in retail for goods being stolen, going missing, magically ending up in the pockets of the employees sometimes. Essentially it’s stock loss, and stock loss has a direct impact on cost because obviously this is inventory that you’re purchasing, that you’re spending money on but not getting any money back from. And Big Data can help.
Focus on shrinkage is a big driver in most retailers and certainly very much falls in the category of saving money. All retailers do a lot of work around shrinkage already. They have big teams in some cases that deal with it purely but in this case, the business goal was to understand the shrinkage drivers and to validate some hypotheses about why shrinkage is happening, and then to actually reduce the lost due to shrinkage, so to provably show that we can find the source of shrinkage, make some changes and fix it. Also, within this particular organization, their analytical sophistication was low so they wanted some easy-to use tools, and they wanted the answers, etc.
The appropriate use of information and analytics will be critical to achieving our shared economic and environmental goals, especially given the urgency of climate change. Bringing American energy into the 21st century is imperative – and an incredible opportunity.
How Your Sensor Data and The Internet of Things Can Save You A Lot Of Money
The Internet of Things, Industrial Internet, Internet of Everything, no matter how you name it, the upcoming connected world will change everything and create massive amounts of sensor data.
For manufacturers, the Internet of Things (IoT) will mean using sensor data to optimize manufacturing processes and improving products.
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