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Big data and bad data: How SMEs can improve the quality of their data

In an increasingly digital world, the effective use of business data is becoming a major tool to help businesses large and small be competitive. Businesses hold a vast range of data, the value of which may remain untapped.

Emerging and maturing technologies that rely on good-quality data, such as machine learning and artificial intelligence, present opportunities for businesses of all sizes to make better use of the data in their possession.

A growing variety of analytics tools is helping businesses make sense of and gain value from the data they hold. This includes tools for big data, data that is too large or complex for traditional data processing tools.

While some of the more advanced analytics tools and technologies may be out of financial reach for many SMEs, small businesses have more cost-effective options that can still generate value from data.

Generating value, however, depends on the quality of that data. Data is an important resource for retaining existing customers and reaching out to new ones. If it’s incomplete, inaccurate or irrelevant, it can harm businesses by creating problems down the line, such as system inefficiencies (e.g. a large proportion of marketing emails bouncing back), annoyed customers or wasted time due to repetitive actions.

The MIT Sloan Management Review has previously suggested that this ‘bad data’ can cost businesses as much as 25 per cent of revenue. And this figure can be much higher still if bad data is used as the basis for costly projects around machine learning and artificial intelligence. Making decisions and taking action based on information that isn’t correct can result in damaging consequences for the business.

Business data must therefore be accurate, complete and reviewed frequently. And with the EU’s General Data Protection Regulation (GDPR) meaning tighter rules around the use of customer data, businesses need to purge this type of data when appropriate, to ensure compliance.

In addition to good data management practices, data security is another important element in maintaining the quality of data. If ransomware gets into your system, for example, it could lock down or encrypt your data, rendering it useless unless you pay a ransom.

Some tips for how SMEs can ensure their data is of sufficient quality include:

  • Create an organisational culture that puts data quality management at the centre of how things are done. This includes providing the appropriate training to relevant staff.
  • Reduce manual effort around data collection and processing. This work is repetitive and time consuming, meaning mistakes and errors can become an issue. Automating some or all of the process will improve accuracy and efficiency.
  • Monitor and cleanse data – this verifies data against statistical measures and uncovers relationships. It also checks the uniqueness of data and analyses its suitability for reuse.
  • Centrally manage your metadata. With multiple individuals – often in different locations – gathering and cleaning data, certain data terms and concepts can be misinterpreted. Centralised management of metadata reduces inconsistent interpretations and establishes corporate standards.
  • Regularly verify that data is consistent with the data rules and business goals and communicate data quality metrics to every stakeholder. This maintains discipline around data quality across the organisation.

If small businesses treat their data as the valuable commodity it is, and take the right steps to ensure its quality, there is no reason why they can’t exploit it to improve operations and boost their competitiveness.

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Big data and bad data: How SMEs can improve the quality of their data
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