Important and redundant information/data keeps on piling up over time, and needs to be managed. This data can be transactional, customer related, or any other type. In order to effectively deal with data, especially when heightened redundancy is reached and businesses start to suffer from operational and efficiency challenges, it all comes down to analytics. The need for data consistency, accuracy, coherence, accessibility, uniqueness, and searchable storage becomes crucial for all types of businesses, but how do you know which end is up?
In terms of analytics, the process of collecting, organizing and analyzing large sets of data (called Big Data) to discover patterns and other useful information is what makes a business work. Typically, it’s been essential to break down IT infrastructure into silos between platforms, networks, and so on. The downside here is it does not give a comprehensive view of the information available in the company, only a part of it.
The solution? Blended analytics. According to Information Management, blended analytics is “about bringing in, correlating and analyzing data together from multiple data sources (APM (News - Alert), network, log, configurations, etc.), using a combination of methods to draw intelligent correlations and extract useful insights from IT operations data.”
For companies, this can mean better customer service.
Blending different types of data sources helps companies better understand their customers and in turn, design targeted services and experiences resulting in greater service levels, loyalty and profitability. Combining operational data from other sources is generating a lot of discussion as a “next step” for companies dealing with Big Data. It lets you handle the data integration, data quality, metadata management and data governance together.
Technology jargon aside, it comes down to this simple idea: blended analytics are a way to overcome technology silos so you can gain more insight into your existing customers, your business processes, and improve upon everything.
Data management is crucial to business function. In finance and banking, data is used to create accurate risk models for loans and mortgages. In marketing, data is used to improve conversions, increase customer satisfaction and create targeted advertising campaigns. Retail stores use customer shopping details to optimize the layout of their stores in order to improve customer experience and increase profits.
How this data is accessed, stored, and analyzed is imperative for efficiency and productivity. How are you analyzing your data?
Edited by Rory J. Thompson