We all know that data is exploding and in more places than ever before. Without the right strategy in place, it can be a real monster. When tamed, data holds the key to great insights about an organization’s business that could help grow sales, improve the customer experience and save a lot of money.
Unfortunately, in real life we don’t have an ‘Analytics Man’ superhero that can turn data into insight magically with the snap of a finger. However; with the right IT strategy in place, data can save the day!
In that spirit, here are five ways to help tame your data:
Adopt a Logical Data Warehouse. I talk to customers all the time who are facing huge expenses to add capacity to their existing enterprise data warehouses (EDW), the place where data is traditionally stored. One customer shared that one terabyte of data in an EDW costs $100,000 per year to maintain, compared to $1,000 in Hadoop. That is a HUGE difference. By implementing solutions to offload infrequently used data from the EDW to Hadoop, it can free up resources to be utilized in more strategic ways. Data is now living in more places but that is okay, because data integration software provides a unified view to make it appear as if it is all in the same place.
Automate Data Processes. Business stakeholders within any organization need intelligence to do their jobs. At Cisco, our IT team developed a big data analytics solution that processes 1.5 billion customer records daily to help our sales team identify potential opportunities. To feed the sales team with information required, IT must coordinate multiple data processing jobs from multiple applications and sources. To do this manually is time consuming and expensive. With a workload automation tool, all these applications, processes, and systems are connected and automated, simplifying the orchestration and viewable through a single pane of glass.
Perform Analytics at the Source. Traditionally, analytics required all data to be moved to a central location to be analyzed. With the amount of data being created in multiple places, moving data to a central location for analysis is difficult, expensive and time-consuming. The future will consist of solutions that involve doing analytics at the edge, without the need to move device data to a central place for analysis. Organizations who can do analytics at the edge of the network, improve their ability to make decisions quickly by doing the analyzing where the data is created.
Combine Data Sources for Value. Supplementing analytics (i.e. Wi-Fi, video) at the edge with analysis of historical data can empower decision making throughout multiple levels of an organization – locally, regionally and even globally. For example, a local store manager can make stock ordering decisions based on the real-time availability of current stock and sales patterns. At a regional or global level, with access to stock and sales patterns in all stores combined with historical sales trends, an organization can make decisions to boost production of an item that is continuously out of stock or adjust the pricing of an item in an effort to increase sales.
Empower Business Users with Self-Service Access. It’s no surprise that data from different sources – sensors, documents, the web and conventional databases – all come in different formats. Before we can ask a software algorithm a specific question, all of the data must be cleaned up and converted into a form that the algorithm can understand. This work has traditionally been done by Data Scientists and can consume the majority of their time. Luckily, end-user applications like Data Preparation, removes this barrier for business analysts. It automates the difficult work of cleansing, combining and enriching data, without the need for coding or scripting by a Data Scientist.
Interested to hear more about Cisco Data & Analytics and how our customers are turning their data into superheroes? Join us at the 2015 Data & Analytics Conference in Chicago later this month. To stay on top of all Cisco Data & Analytics news and highlights, check out our new blog: Analytics & Automation.
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