Actian Blog / Do You Have Big, Fast, Jumbled, Useless, or Ugly Data? Here’s What to Do

Do You Have Big, Fast, Jumbled, Useless, or Ugly Data? Here’s What to Do

Unstructured Data

It has been more than 20 years since Meta Group (acquired by Gartner) introduced the 3Vs of data, Volume, Velocity and Variety. Gartner later expanded the 3Vs to 5Vs by adding Value and Veracity. To this day, these remain important considerations in data analytics. However, their size and complexity continue to increase. Here’s a look at where we are today and some pointers for how to keep up.

Illustrated infographic about the 5 Vs of data

Volume

The volume of data refers to the size of data that needs to be analyzed and processed. Data is getting bigger. IDC predicts that the global data volume will expand to 175 zettabytes by 2025. Over half of this, 90 zettabytes, will come from Internet of Things (IoT) devices. Moreover, Forbes predicts that 150 trillion gigabytes of real-time data will need analysis by 2025.

Pointers:

  • Look for solutions that can scale with data volumes
  • Make sure that you can reuse data pipelines and share data across use cases
  • Choose real-time analytics that can meet your key performance indicators and service level agreements
  • Evaluate the solution’s ability to offer consistent management, governance, and compliance

Velocity

Velocity refers to the speed with which data is generated. Data is getting faster, especially with the increased analysis of real-time data streams to tackle diverse use cases such as IoT sensor data analytics, fraud detection, online advertising, cybersecurity, log analytics, stock trading and much more. IDC estimates data generated from connected IoT devices will be 79.4 zettabytes by 2025, growing from 13.6 ZB in 2019.

Pointers:

  • Preprocess data at the edge to reduce the cost and effort of moving and storing data
  • Test high speed data load capabilities of your data analytics so you can quickly access operational and streamed data

Variety

Variety refers to the number of types of data and includes structured, semi-structured or unstructured data. Data required for analytics is getting more jumbled. Unstructured data, content that does not conform to a specific, pre-defined data model, is rapidly surging. IDC forecasts that 80% of global data will be unstructured by 2025. About 90% of unstructured data has been created in the last two years. Organizations analyze just .5% of unstructured data today, but this will certainly increase soon. Semi-structured data such as JSON, XML, and HTML formats is also dramatically growing due to growth of the web.

Pointers:

  • Assess the solution’s ability to make data accessible regardless of structure or format
  • Look for the flexibility to create custom connectors and extend integrations

Value

Value refers to whether data is positively impacting a company’s business outcomes. Unfortunately, data is often useless. The reason is simple; data doesn’t meet the needs of users. Forrester finds that less than 0.5% of all data is ever analyzed and used. It also estimates that if the typical Fortune 1000 business was able to increase data accessibility by 10%, it would generate more than $65 million in additional net income.

Pointers:

  • Get to know what data your users really need
  • Prioritize data that will help users meet their goals
  • Understand issues that may prevent users from getting insights they need
  • Present data in a manner that is timely and in the right context

Veracity

Veracity refers to the quality and credibility of data. Data can be ugly and decisions made on it cost you money. According to Gartner, the financial impact poor quality data has on an organization is around $15 million in losses per year on average.

Pointers:

  • Ensure your data quality is adaptable and scalable
  • Include a level of automation that can help filter out quality data and better integrate it across the enterprise
  • Take a collaborative approach to data quality across the enterprise to increase knowledge sharing and transparency regarding how data is stored and used

Want to learn more about data challenges and trends? Here are some related resources that you may find useful:

Blog: Semi-Structured Data: What It Is and Why It Matters
Blog: What is the Future of Data Quality Management (DQM)?
Report: Operational Data Warehouse Basics for Dummies

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Avalanche Cloud Data Platform delivers, including enterprise-proven data integration, data management and data analytics. She enjoys applying her extensive knowledge in these areas to help customers find solutions that will help them achieve long-lasting success. Teresa brings a proven 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware. She was also Datameer’s first VP of Marketing for big data analytics built on Hadoop, and has served as VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from the MIT Sloan School of Management and software engineering from Harvard University.

facebooklinkedinrsstwitterBlogAsset 1PRDatasheetDatasheetAsset 1DownloadForumGuideLinkWebinarPRPresentationRoad MapVideo