Companies are just now beginning to harness the power of big data for the purposes of information security and fraud prevention.
Only 50% of companies currently use some form of analytics for fraud prevention, forensics, and network traffic analysis.
Less than 20% of companies use big data analytics to identify information, predict hardware failures, ensure data integrity, or check data classification, despite the fact that by doing so, companies are able to improve their balance of risk versus reward and be in a better position to predict potential risks and incidents.
Businesses today understand that data is an important enterprise asset, relied on by employees to deliver on their customers' needs, among other uses of data such as making business decisions and many others. Yet too few organizations realize that addressing data quality is necessary to improve customer satisfaction. A recent Forrester survey shows that fewer than 20% of companies see data management as a factor in improving customer relationships. This is very troubling number.
Not paying attention to data quality can have a big impact both on companies and the customers they serve. Following are just two examples.
There has been a lot of talk lately about big data. What is big data?
Big data is is a collection of data sets so large and complex that it becomes difficult to process using on-hand commonly used software tools or traditional data processing applications. The challenges include capture, governance, storage, search, sharing, transfer, analysis, and visualization.
What is considered "big data" varies depending on the capabilities of the organization managing the data set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain.
Big data sizes are a constantly moving target. As of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. How does it apply to us and what we do in content management?
A data lake is a large storage repository and processing engine. Data lakes focus on storing disparate data and ignore how or why data is used, governed, defined and secured.
Benefits
The data lake concept hopes to solve information silos. Rather than having dozens of independently managed collections of data, you can combine these sources in the unmanaged data lake. The consolidation theoretically results in increased information use and sharing, while cutting costs through server and license reduction.
During last ten years the volume and diversity of digital information grew at unprecedented rates. Amount of information is doubling every 18 months, and unstructured information volumes grow six times faster than structured.
Big data is the nowadays trend. Big data has been defined as data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.
In my last post, I described Teradata Unified Data Architecture™ product for big data. In today's post, I will describe Teradata partner Alteryx which provides innovative technology that can help you to get the maximum business value from your analytics using the Teradata Unified Data Architecture.™
Companies can extract the highest value from big data by combining all relevant data sources in their analysis. Alteryx makes it easy to create workflows that combine and blend data from relevant sources, bringing new and ad hoc sources of data into the Teradata Unified Data Architecture™ for rapid analysis. Analysts can collect data within this environment using connectors and SQL-H interfaces for optimal processing.
Data complexity in companies can be a big obstacle to achieve efficient operations and excellent customer service.
Companies are broken down into various departments. They have hundreds, thousands, or even hundreds of thousands of employees performing various tasks. Adding to the complexity, customer information is stored in so many different applications that wide gaps exist among data sources. Bridging those gaps so every employee in the organization has a consistent view of data is possible and necessary.
Big Data is an ever-evolving term which is used to describe the vast amount of unstructured data. Published reports have indicated that 90% of the world’s data was created during the past two years alone.
Whether it’s coming from social media sites such as Twitter, Instagram, or Facebook, or from countless other Web sites, mobile devices, laptops, or desktops, data is being generated at an astonishing rate. Making use of Big Data has gone from a desire to a necessity. The business demands require its use.
Successful companies know that analytics is the key to winning customer loyalty, optimizing business processes and beating their competitors.
By integrating data from multiple parts of the organization to enable cross-functional analysis and a 360-degree view of the customer, businesses can make the best possible decisions. With more data and more sophisticated analytics, you can realize even greater business value.
Big Data is everywhere. But to harness its potential, organizations should understand the challenges that come with collecting and analyzing Big Data. The three values that are important in managing big data are volume, velocity, and variety. These three factors serve as guidance for Big Data management, highlighting what businesses should look for in solutions.
But even as organizations have started to get a handle on these three V’s, two other V’s, veracity and value are important as well, if not more so.
Will 2015 be the year that your enterprise be able to finally harness all of that customer data that they have compiled over the years? Will there be ways to organize and use this information to impact the bottom line? Indeed, this data has become a form of capital for enterprises. So what will change in 2015?
Big Data Brands to Watch
Here are the areas to watch: secure storage and backup with encryption, reliable data management and data visualization (DV) are key ingredients as far as next generation big data software is concerned.
As far as vendors are concerned, there are several players in the space including Twitter-owned Lucky Sort, Tableau, Advanced Visual Systems, JasperSoft, Pentaho, Infogram Tibco, 1010 Data, Salesforce, IBM, SAP, Hewlett-Packard, SAS, Oracle, Dell and Cisco Systems. These are a mix of independent and majors, but all have solid reputations in the industry. Choosing which one depends on numerous factors like budget, IT systems already in place, preference, reaquirements, etc.
