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The Big Data Divide | @CloudExpo #BigData #IoT #IIoT #M2M #IternetOfThings

Business users must be able to quickly and easily access the data they need to make more informed business decisions

Within enterprises - and even small businesses - there are two distinct groups with differing goals, needs and objectives when it comes to business intelligence and analytics strategies. Business users are tasked with analyzing Big Data to help their companies make timely and more meaningful decisions, and as such, require immediate access to a wide variety of sources, including structured, semi-structured, unstructured and streaming data. On the other side of the house, IT professionals are tasked with storing and securing massive data stores, as well as ensuring regulatory compliance of corporate information. Therefore, they prefer to make information available on an as-needed basis, rather than creating an environment of open access. So the tug-of-war game begins.

In the self-service analytics world we live in, business users must be able to quickly and easily access the data they need to make more informed business decisions. The problem is that the data that holds the most analytical value is often locked away in data stores protected by IT or housed within multi-structured documents, such as PDF files, POs and third-party reports that require IT intervention. Consequently, business users have no choice but to rely on IT to get the data they need for analysis, and the lag time between request and delivery often results in decisions being made based on old or incomplete data.

Big Data access and real-time analysis are essential for reliable company operations. With business users and IT departments at odds, many organizations are left wondering how to bridge the gap between the ease of use and agility that business users demand and the automation, scalability and governance required by IT.

The changing relationship between IT and business users
The rise of Big Data has changed how IT and business users interact and work together. Business intelligence used to be a centralized, IT-led initiative, but business users quickly became frustrated because they couldn't access the information they needed fast enough.

To remain competitive, organizations must enable business users and citizen analysts to quickly and easily access and prep data for analysis, visualization or for other business processes. Data users can no longer afford to wait hours, days or weeks for their requests to be fulfilled.

Many organizations now realize that an IT-managed, centralized approach to business intelligence and analytics does not provide the agility necessary for business users. But providing business users with self-service analytics capabilities can introduce serious governance risks - especially given the fact that half of the data business users access for analysis comes from sources that aren't managed by IT.

While most organizations have well-defined strategies for governing data that lives in managed systems like enterprise applications or data warehouses, analysts often need to pull data from non-managed sources as well, like CSV or text extracts from transactional systems, personal spreadsheets, third-party reports or semi-structured content. Without proper governance in place, this can create big headaches for IT. IT must be able to properly track and secure these non-managed data sources and ensure regulatory compliance.

What's to be done? Business users must be self-sufficient, but in a way that brings management and governance to the process to satisfy IT requirements. This is the role self-service data preparation solutions play today, and it is why this technology has become a key component in Big Data and analytics strategies.

Bridging the gap
Self-service data preparation tools are rapidly being recognized as a necessary component of any data discovery or advanced analytics implementation. More advanced, bi-modal data preparation solutions empower business users to retrieve, blend and prepare various data sources, but do so within a central content repository that allows for secure storage, management and access control to all source content as well as any reusable models for data extraction and prep routines.

Further easing IT's anxiety, self-service data prep solutions offer the following governance capabilities:

  • Data Retention - Documents version control for consistency. Additionally, to meet regulatory and business requirements, relevant source data and documents should be archived.
  • Data Masking - Prevents data breaches, especially those caused by internal employees, by hiding or obfuscating original data with random characters. The data is still usable for analytics, while the underlying data is visible only to authorized users.
  • Data Lineage - Drills down into any source or document for data reconciliation or auditing.
  • Role-based Access - Segments prepared data sets based on user roles to ensure the right subset of data is delivered to authorized users.
  • Auditing - Tracks information access for complete audit logging and reporting.
  • Data curation - Share frequently used data sources or automated data preparation routines with other analysts allowing for consistency in the data used to make important business decisions.

Telling the whole story - the right way
To yield maximum return on investment from business intelligence and analytics investments, organizations must empower business users with the speed and agility they require to access all the data they need to tell the whole story. But they must do so in a way that satisfies IT's need for security, governance and compliance. Fortunately, self-service data prep technology has turned these two previously mutually exclusive requirements into a reality for organizations of all sizes.

It's estimated that only 12 percent of data1 is being used today to make decisions. Self-service data prep enables businesses to tap into the remaining 88 percent of unused data to make timely, more meaningful decisions. When you have good - and complete - data, you can then begin to predict what is likely to happen and respond faster when it does.

1 The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014, http://ibm.biz/Bd4DJC

More Stories By Dan Potter

Dan Potter is chief marketing officer at Datawatch Corporation. Prior to Datawatch, he held senior roles at IBM, Oracle, Progress and Attunity where he was responsible for identifying and launching solutions across a variety of markets including data analytics, cloud computing, real-time data streaming, federated data and e-commerce.

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