Getting accurate real-time data is essential to business. It requires identifying patterns using metadata, grouping this information through metadata files, and ensuring metadata accuracy through a metadata management framework.
Consider real-time data accuracy, a data quality characteristic referring to correct values and formats, a must for a profitable and growing business. Companies need accurate real-time data to cope with influxes in consumer demand. A third of C-suite executives and financial professionals agree that they value their real-time financial data more and more.
Unfortunately, many organizations continue to struggle to get business insights from their real-time data. Companies collect hours upon hours of streaming data feeds but may only need a couple of minutes of this stream to make a good decision.
Even deciding how to validate volumes of data amassed causes challenges in quickly figuring out whether one data set has more accuracy than another. Make the wrong call and contribute towards a business losing $14 million.
Fortunately, metadata, data about data, promises help in getting to real-time data accuracy. This article will start the reader on a journey using metadata to get real-time data accuracy.
Using Metadata to Validate Real-Time Data Accuracy
It may seem counterintuitive to talk about generating more data to help keep existing data accurate. But metadata has several superpowers:
- If it is accurate, metadata provides context about the information it describes, proving data accuracy. A computer program can turn this metadata into a schema or representation.
- Metadata definitions contain any combination of technical, business, or operational descriptions of data sets. So, systems, processes, or people can use metadata to characterize customers, purchases, products, locations, etc.,
- Algorithms can use technical metadata to flag another process or person to take further action.
- Metadata can wrap around a massive chunk of data, making that data more findable.
Two experts, Romero and Calders, have leveraged these metadata strengths with an approach called information profiling. They propose a framework applying metadata to form a schema for profiling and then enclosing the results using metadata.
Taking these findings further, companies can use their business rules to guide metadata formation of the schema and group data in packets wrapped by metadata. These organizations would have a more efficient way to deal with real-time data by employing algorithms to look for data patterns and retrieve data sets that match.
Exploring a real-time data accuracy example
How would the use of metadata for real-time data accuracy look in real life? Gordon and Shankaranarayanan, from Babson College, provide insight.
Say that Company A’s feed has hundreds of instant messages coming in each hour. A customer, John Doe, texts he wants to buy a bike from company A. Company A wants to validate these messages are from John Doe and that he wants to buy a bike.
First, an application would create a John Doe profile, metadata about John Doe. Say the metadata about John Does has values based on phone number, the type of mobile phone used, and membership status with Company A. This John Doe representation would be good as the data contained in Company A’s customer relationship management system.
Then a computer program takes this John Doe profile and matches it with the text streaming in the feed, with business rules defining what to look for in the data stream. Once the software sees data matching John’s schema Doe, it will bundle it together under a content metadata file. This content metadata file would be searchable by people who would be assured the text messages came from John Doe (if the metadata quality met business needs).
Also, an algorithm would put together a purchasing profile to determine the likelihood of John Doe purchasing a bike, like how Amazon figures out what products to recommend for its customers. Then, an algorithm can apply this purchasing profile and weigh the likelihood John wants to buy a bike.
Making metadata accuracy critical
Note that using metadata to create a schema and batch depends heavily on metadata accuracy. Otherwise, ensuring real-time data accuracy would be impossible.
In the example above, if John Doe had changed his phone number and his metadata still had the old phone number, then John’s metadata would be inaccurate. Likewise, the metadata file with the real-time data sets would need to be labeled correctly. If the metadata with John Doe’s text files had the wrong customer’s name, a person would not find John’s text file.
So, the same reasons and processes companies use to monitor data and cleanse it, to consistently deliver high data quality, also apply to the metadata. Metadata used in validating real-time must be adequately accurate to have confidence about real-time data accuracy.
A metadata management framework
Would it be enough then to automate the metadata monitoring and cleansing? No. Getting enough metadata quality to be valuable and accessible to the business depends on a metadata management framework.
The organization’s people, processes, and technologies make up this metadata management framework and the business rules that create it.
For example, if one department standardizes its customer names in one system and another department formats customer names differently in a separate application, it has a shaky metadata management framework. Which department does the organization take metadata from to use?
Developing a metadata management framework to ensure adequate real-time data accuracy requires:
- Metadata management assessments on what structures exist and their impact. From this baseline, companies can figure out what processes to change.
- Data strategy, a plan for using data to keep the business going and growing.
- Data governance, a program that coordinates metadata access and security, to remain compliant.
- Collective data literacy about the metadata’s importance and value in determining real-time data accuracy. In addition, workers would need to know how to define good metadata.
- Solid business requirements about what attributes and values define what or who the metadata describes.
In 2020, companies saw why they needed real-time data quality while mistrustful about getting business insights from this data. High data volumes and data speeds present businesses with significant obstacles to quickly assure each piece of data has accuracy in a text or message stream.
Curating the real-time data using a metadata schema and business rules obtains more value while saving company resources from analyzing irrelevant data. Chunking the results and labeling these data sets using metadata makes finding accurate data faster. But using metadata in this way requires accurate metadata and an adequate metadata management structure so that the business has trust in its real-time data accuracy.