Need faster analytics? Modernize your data management platform.

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businesswoman contemplating modernized data managementWhen an organization intends to use big data to become smarter and more competitive, the standard approach is to modernize its analytical capabilities. A modern analytical application promises to generate more insight and help you spot new market trends faster than before. But finding insights faster than others in the age of big data is not only about implementing a new piece of visualization software – it's a transformational journey for people and for the entire analytical process. If we think of analytics as the engine that accelerates a smart organization, then data is the fuel that runs the engine. In other words, to accelerate analytics, organizations need to improve their data supply.

If you modernize your data management platform, it speeds up the analytical life cycle – because modern data management techniques enable you to access data faster and prepare it more efficiently. This approach ensures that analytics users have the right data at the right time. Simultaneously, it establishes a foundation for the ever-increasing data demands from analysts and business users.

Why you should modernize your data management to support a state-of-the-art analytics platform

Data preparation is one of most difficult and time-consuming challenges for those who use data discovery tools and analytical platforms. Such users spend about 80% of their time preparing data – which starts with identifying the business problem and continues until the analyst uncovers an insight. There are many reasons for this, such as issues with data availability, data quality, data readiness, data accuracy, data structure and so forth.

When modernizing your analytical platform, the goal is to increase efficiency and reduce the amount of time it takes to get from the initial question to the new insight. The largest potential for efficiency gains lies with the data preparation stage, because it is the most complex and time-consuming part of the process.

If you modernize your data management platform, you can provide capabilities to save significant time during the data access and data preparation phases. The time saved gives the analyst more time to solve the next business problem. If the data management infrastructure is unable to match the speed and maturity of the analytical platform, then only a fraction of its capabilities can be used – and new insights will remain hidden in the data.

How can data management make the transition to a modern analytics world?

Let's take a look at five main characteristics of a modern data management solution. All of these are crucial to supporting modern analytical platforms.

Manage all the data when you modernize your data management

It's not only about the volume of data. All data refers to the three V'’s: volume, variety and velocity. Modern data management software is designed to process all the different varieties of data that's available these days, including huge volumes of structured and unstructured data. Whether it's social data, streaming data or text data, all types of data are valuable sources for helping you to better understand customers, products and markets. A state-of-the-art data management solution enables you to effectively manage all of these data types in a single solution. By combining a modernized data management solution with a modern analytical platform, you can elevate your business by capturing more insights from your data. And you can do it in a more efficient way.

Get faster data access and integration

A modernized data management software solution is about rapid data access and integration. Such solutions provide fast, innovative ways to access and prepare new data sources. Intuitive data access components combined with easy-to-deploy data preparation routines allow data developers and even nontechnical users to quickly make use of new data for analytical purposes. This is a key element in helping businesses become more agile, because it saves time and positions them to make better-informed decisions.

Enable self-service

Self-service data management empowers nontechnical business users to be more self-sufficient while freeing up IT resources from simple data management tasks. But self-service analytics based on traditional, IT-driven data management is like driving a car with the parking brake on. Transforming the business to be more self-sufficient with analytics requires the business to be self-sufficient with data access and data preparation.

If you modernize your data management platform, you can provide interactive, purpose-built data management capabilities for your business users. This type of solution supports:

  • Finding the right data.
  • Querying the data.
  • Filtering, transforming and standardizing the data.
  • Moving the data into the analytical application.

Using self-service data management features, business users can quickly assess new data sources and variables as they search for hidden insights.

Put modernized data management everywhere

Today’s modernized data management solutions deploy data preparation, data quality and data transformation processes on the edge, in stream or in distributed data platforms like Hadoop. New data types like streaming data, social media data, text data and big data make it necessary to employ new methods and technologies so you can deploy data management routines where they're most appropriate. Instead of moving the data to a data management server, a modern data management application moves processing to where the data lives. This makes data processing for analytics faster and more effective.

Govern data

Data governance may seem like a burden to both IT and business users. But it's a key enabler – for sharing data, for doing self-service data management and for improving collaboration between IT and business. Data governance is actually the starting element of the transformation journey. If you modernize your data management platform, you'll be able to build common data standards and definitions to generate a uniform understanding and ensure correct use of the data. By enforcing proactive data monitoring and cleansing routines – and by implementing new methods of collaboration and sharing – organizations can improve their trust in data, ease the data preparation process and enable the pervasive adoption of analytics.

SAS Data Management – A modern platform for today's analytical needs

SAS combines comprehensive data management capabilities and sophisticated analytics to empower nontechnical users and data professionals alike. Our solution allows all types of users to collaborate on all types and sizes of data-intensive projects.

The multifaceted capabilities of SAS Data Management serve different types of users by providing dedicated features for each. At the same time, these capabilities support IT by enabling them to implement and manage a single data management solution that works seamlessly with our analytical platform. Customers depend on SAS Data Management to quickly and reliably provide high-quality, tightly governed data to a wide range of operational, modernization, migration and analytics projects.

Download a paper to learn how SAS Data Management solves real-world challenges
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About Author

Helmut Plinke

Principal Business Solutions Manager

Helmut Plinke acts as Principal Business Solutions Manager for SAS, focusing on data quality and data governance technologies. Helmut is an enthusiast of data quality technologies to improve fitness of data and thereby help businesses to improve efficiency and gain competitive advantages. In his current role Helmut supports customers in designing enterprise data management solutions based on SAS technology. He is a specialist in data quality and data integration technologies for a long time now and has been part of some of the major SAS data quality and data governance projects in DACH and the Netherlands recently. With over 15 years of experience across multiple industries Helmut has also gained a wealth of knowledge and experience in technologies like business intelligence, content management and enterprise application integration from his past roles with other companies. Helmut has published in IS Report and speaks about the topic of data management at SAS and public conferences sharing his project experience and knowledge.

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