Effective data analysis demands careful data management. If an organization is unable to obtain a snapshot of the business at any given moment, this is likely the result of poor data management. The business won’t have the necessary insight for planning and taking action, thus impeding the decision-making process, business performance, and the ability to predict and forecast.
Organizations today are generating an increasing amount of data; they are processing more data transactions, and there is more interaction between systems—both internal and external.
In many organizations, data comes from an increasing number of (and increasingly disparate) sources. These sources can be enterprise resource planning (ERP) systems, customer relationship management (CRM) applications, workforce automation systems, etc.
Three Fundamental Questions
While organizations are eager to grow, they may risk losing control over the business and its infrastructure if the expansion of corporate applications is not effectively managed. This is especially true regarding applications that enable data analysis or help create frameworks for strategic planning and decision improvement.
If your organization wants to improve its data analytics technologies, you need to address three fundamental questions:
- How effectively are you handling your data?
- What are your urgent data management needs?
- How do you evolve from handling your organization’s data reactively to handling it proactively?
Consider the maturity level of your data management infrastructure and strategy. This will help you to describe, explain, and evaluate the growth cycle of your current data management and analytics infrastructure across the different stages of the data management process. Identifying your current data analytics capabilities will serve as the basis for evolving them and establishing how to address specific high-priority needs.
Assessing Your Data Management Maturity
The following questionnaire can help you determine how well your organization is managing its data. It provides an overview of some of the criteria to consider when assessing the maturity of your current data management and analysis platform solution. Click on the image to expand and print.
Organizations typically fall into one of four broad categories in terms of the technological framework they have for handling, processing, and analyzing big data.
There is a basic or no formal implementation of data management and analytics processes. Data is collected and treated in raw form, and there is lack of data quality, which frequently causes frustration. The information generated is often not of adequate quality to guide the organization’s business strategies or business performance improvement. It mainly serves for accountability purposes.
There is a basic or more formal data management and analytics process in place, and data is treated with a basic and systematic approach. Still, data flows too slowly to be useful. The organization’s data management and analytics process is limited to reacting to actual and/or historical conditions. Data is gathered from internal sources, which does not reflect all the levels of information required for improving the performance of the business.
There is a solid data management and analytics strategy in place. The cycle from data collection to information generation is automatic, and many of the processes for this purpose have already been established and improved upon.
Organizations are now starting to do more than just review historical information. They can analyze scenarios, do basic predictions and forecasting, and implement new technologies for these purposes. Organizations at this stage have come to realize the importance of data as a valuable asset, and are frequently working to deploy data-related initiatives (data quality, corporate data management, and data discovery and visualization).
Organizations at this stage already have a robust data management and analytics process in place. Data management initiatives are a common part of corporate life. These types of organizations are now mainly looking for ways to tighten all levels of leadership—from operational to strategic—and are putting special emphasis on aligning their data strategies with their tactical and strategic goals.
There is a search for initiatives that will let them use information as a real competitive advantage by processing data in real time and gathering information from external sources such as social media channels. Organizations at this stage are trying to solve corporate issues such as big data handling and information governance.
Evolving Your Big Data Strategy
Big data analytics is a key component of modern data management practices and can have a real and positive impact on the way organizations make use of data as an asset for improving operations, performance, and decision making.
Today, nearly every organization in every vertical industry is exploring the potential of analytics using large data sets for business intelligence analysis and insights. If you’re ready to evolve your big data strategy, you know that selecting the right analytics solution is both a challenge and an opportunity.
To find out more about big data analytics, including an analysis of the software market, an overview of the different solutions available, and a side-by-side comparison of the key functionality features of major big data analytics software products, check out TEC’s 2016 Business Intelligence Buyer’s Guide focusing on big data analytics.