In today’s big data era, businesses generate and collect data at unprecedented rates. More data should imply more knowledge but it also comes with more challenges. Maintaining data quality becomes harder as the amount of data being handled increases.
It’s not just the difference in volumes, data may be inaccurate and incomplete or it may be structured differently. This limits the power of big data and business analytics.
According to recent research, the average financial impact of poor quality data can be as high as $15 million annually. Hence the need to emphasize data quality for big data management.
Understanding the big data movement
Big data can seem synonymous with analytics. However, while the two are related, it would be unfair to consider them synonymous.
Like data analytics, big data focuses on deriving intelligent insights from data and using it to create opportunities for growth. It can predict customer expectations, study shopping patterns to aid product design and improve services being offered, analyze competitor intelligence to determine USPs and influence decision-making.
The difference lies with data volume, velocity and variety.
Big data allows businesses to work with extremely high data volumes. Instead of megabytes and gigabytes, big data talks of data volumes in terms of petabytes and exabytes. 1 petabyte is the same as 1000000 gigabytes – that’s data that would fill millions of filing cabinets!
Then there’s the speed or velocity of big data generation. Businesses can process and analyze real-time data with their big data models. This allows them to be more agile as compared to competitors.
For example, before a retail outlet can record sales, location data from mobile phones in the parking lot can be used to infer the number of people coming to shop and estimated sales.
The variety of data sources is one of the biggest differentiators for big data. Big data can collect data from social media posts, sensor readings, GPS data, messages and updates, etc. Digitization and the steadily decreasing costs of computing have made data collection easier but this data may be unstructured.
Data quality and big data
Big data can be leveraged to derive business insights for various operations and campaigns. It makes it easier to spot hidden trends and patterns in consumer behavior, product sales, etc. Businesses can use big data to determine where to open new stores, how to price a new product, who to include in a marketing campaign, etc.
However, the relevance of these decisions depends largely on the quality of data used for the analysis. Bad quality data can be quite expensive. Recently, bad data disrupted air traffic between the UK and Ireland. Not only were thousands of travelers stranded, airlines faced a loss of about $126.5 million!
Common data quality challenges for big data management
Data flows through multiple pipelines. This magnifies the impact of data quality on big data analytics. The key challenges to be addressed are:
High volume of data
Businesses using big data analytics deal with a few terabytes of data every day. Data flows from traditional data warehouses as well as real-time data streams and modern data lakes. This makes it next to impossible to inspect each new data element entering the system. The import-and-inspect design that works for smaller data sets and conventional spreadsheets may no longer be adequate.
Complex data dimensions
Big data comes from customer onboarding forms, emails, social networks, processing systems, IoT devices and more. As the sources expand, so do data dimensions. Incoming data may be structured, unstructured, or semi-structured.
New attributes get added while old ones gradually disappear. This can make it harder to standardize data formats and make information comparable. This also makes it easier for corrupt data to enter the database.
Inconsistent formatting
Duplication is a big challenge when merging records from multiple databases. When the data is present in inconsistent formats, the processing systems may read the same information as unique. For example, an address may be entered as 123, Main Street in one database and 123, Main St. This lack of consistency can skew big data analytics.
Varied data preparation techniques
Raw data often flows from collection points in to individual silos before it is consolidated. Before it gets there, it needs to be cleaned and processed. Issues can arise when data preparation teams use different techniques to process similar data elements.
For example, some data preparation teams may calculate revenue as their total sales. Others may calculate revenue by subtracting returns from the total sales. This results in inconsistent metrics that make big data analysis unreliable.
Prioritizing quantity
Big data management teams may be tempted to collect all the data available to them. However, it may not all be relevant. As the amount of data collected increases, so does the risk of having data that does not meet your quality standards. It also increases the pressure on data processing teams without offering commensurate value.
Optimizing data quality for big data
Inferences drawn from big data can give businesses an edge over the competition but only if the algorithms use good quality data. To be categorized as good quality, data must be accurate, complete, timely, relevant and structured according to a common format.
To achieve this, businesses need to have well defined quality metrics and strong data governance policies. Data quality cannot be seen as a single department’s responsibility. This must be shared by business leaders, analysts, the IT team and all other data users.
Verification processes must be integrated at all data sources to keep bad data out of the database. That said, verification isn’t a one-time exercise. Regular verification can address issues related to data decay and help maintain a high quality database.
The good news – this isn’t something you need to do manually. Irrespective of the amount of data, number of sources and data types, quality checks like verification can be automated. This is more efficient and delivers unbiased results to maximize the efficacy of big data analysis.
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