Knowledge is power in today’s information-driven economy. That means the ability to manage and analyze big data is more important than ever for enterprises, regardless of their industry. With the right enterprise analytics strategy in place, they can minimize risk while maximizing the business value buried within their data sets.
What is an Enterprise Analytics Strategy?
Without a strategy in place to manage big data, all of that information will end up doing little more than take up storage space. An enterprise analytics strategy helps organizations to identify the tools and techniques they need to deploy to work with these huge data sets and extract meaningful insights from them that can be used to inform business decisions.
A good enterprise analytics strategy creates a comprehensive vision and end-to-end roadmap for managing and analyzing data. How, for instance, will data be stored after it’s gathered? Where will it be processed? These are critical questions every organization needs to answer if it’s going to make use of the unstructured data at its disposal. The strategy should also establish controls and procedures to help protect an organization from the inherent risks involved with handling data that could contain various forms of private information.
There are a few key elements to a strong enterprise analytics strategy:
It should be practical enough to be implemented on a wide scale and incorporated into existing functions.
It should be specialized enough to meet the relevant needs of the organization rather than being a generic “off-the-shelf” solution.
It needs to be able to change over time to meet evolving business needs.
It needs to integrate with existing data management solutions and be able to accommodate future solutions.
8 Reasons Why You Need an Enterprise Analytics Strategy
1. Risk Mitigation
Collecting any kind of data brings both value and risk to any enterprise. Understanding the security implications of gathering, managing, and analyzing data is critical to maximizing its value while minimizing the risks of a data breach or a compliance violation. An enterprise analytics strategy helps to identify potential vulnerabilities and ensures that any big data tools implemented will meet all relevant compliance requirements.
2. Consistent Data Policies
An enterprise analytics strategy helps organizations to map out their data management architecture across multiple enterprise systems. Once a strategy has identified where information assets are being gathered and where it’s being stored, it can be used to create a logical plan for segmenting and analyzing data that improves efficiency and performance.
3. Eliminate Unnecessary Data
Unstructured data contains remarkable insights that can help an organization make better business decisions, but some portion of it is just meaningless noise. An enterprise analytics strategy can identify what sources and types of data actually matter and can establish automated processes to eliminate redundant or unnecessary information. This can help save on storage costs for both hardware and cloud resources.
4. Set Data Priorities
Many organizations are gathering data simply because they have the ability to do so, often taking it from sources that may not be all that meaningful or even increase risk exposure. An enterprise analytics strategy lays out the full scope and complexity of an organization’s data landscape, identifying all sources of data and the life cycle for managing it. With this map in place, they can make choices about what data sources should be priorities and what gaps need to be addressed to provide a more complete picture of the marketplace.
5. Establish Responsibility and Accountability
It’s easy to forget that there is a human element to data management and analytics. Someone within the organization needs to be responsible for how data is handled at every step of the management process. Establishing these lines of accountability not only help employees to understand their role in managing data, but also identifies gaps in security controls that could result in a data breach.
6. Phase Out Legacy Data Systems
Data management and analysis is a rapidly changing field that requires organizations to remain on the cutting edge of technology. Taking advantage of the latest analytics tools often means moving on from outdated hardware and applications. An enterprise analytics strategy can provide a roadmap for transitioning away from legacy systems through a combination of upgrading assets and migrating to cloud-based solutions without disrupting business continuity.
7. Improve Data Quality
Like any other tool, analytics platforms are only as effective as the data being fed into them. By understanding what it hopes to accomplish with data analysis, organizations can implement policies to improve the overall integrity and quality of their source data. This could include eliminating redundancies, improving source documentation, or addressing gaps/inconsistencies in data sets.
8. Identify the Right Tools
Big data analysis is not a “one-size-fits-all” undertaking. No single analytics tool can identify all the insights and trends an organization needs to know about to make better decisions. This is especially true of a large enterprise that gathers data from multiple sources and has multiple departments with very different business objectives. An enterprise analytics strategy can help them to identify which big data tools must be implemented to meet these diverse needs. It can also help to integrate these solutions within a single, enterprise environment to prevent fragmentation into isolated silos.
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