Today’s enterprises are spending billions on big data and analytics solutions, and even more on building technology environments to support them. According to IDC Research, companies around the world will invest nearly $275 billion per year in data and analytics by the end of 2022. Digital transformation – and the ways it can enable data-driven decision-making across the business – remains top-of-mind for leaders seeking to innovate in order to remain competitive in a fast-changing and rapidly digitizing business climate.
But without access to clean, high-quality data, these initiatives are doomed to fail. Researchers at IBM estimate that poor data quality costs companies $3.1 trillion per year in the U.S. alone. The reality is no matter how much an organization spends on data systems, they’ll still produce garbage if you put garbage into them. There’s little doubt that improving data quality presents a massive opportunity for cost savings and improved business intelligence.
What is data cleansing?
Data cleansing is a vital stage in preparing data for analysis. In general, it consists of identifying incomplete, inaccurate or irrelevant records within a data set and then replacing, modifying or deleting those records. If data cleansing is effective, all data sets should be consistent across the enterprise, and all should be error-free.
Because data is the fuel for business decision-making today, ensuring its quality helps the business make better strategic choices. Data quality also reduces wasted effort (The sales team, for instance, won’t spend time cold-calling prospects at the wrong phone number), and thus streamlines business processes. This improves overall operational efficiency.
Researchers identify several criteria that should be met in order to classify data as high quality. These include:
- Validity: Does the data conform to pre-specified business rules or constraints? These can include data ranges, maximum or minimum values, or limits such as ‘this field cannot be empty.’
- Accuracy: How well does the data represent the truth? How closely does it match what’s been measured or recorded in the real world?
- Completeness: Is the data set thorough and comprehensive?
- Consistency: Are measures equivalent in multiple data sets across the enterprise?
- Uniformity: Are the same units of measure used in all systems?
- Timeliness: Is the data recent enough to retain value and relevance?
Data cleansing can include manual or automated processes, or both. Its goal is to transform “dirty” data – or data of uneven quality – into data that’s in a high-quality state.
5 Steps to better-quality data
Cleaning a single, small-scale data set manually isn’t an onerous task. But ensuring an enterprise has the right governance processes and business rules in place to remove the majority of errors from the majority of data sets, most of the time, requires consistent effort and buy-in from leadership, especially as organizations collect ever-growing amounts of data. To find the root cause of systemic errors, you’ll need a semantic understanding of the business, as well as its data modeling and analytics needs.
With that in mind, here are some general steps that data teams and business stakeholders can follow to improve data quality in their organization.
No. 1: Correct data errors at the source, or as early as possible.
The earlier in the data collection process errors can be fixed, the fewer times they’ll be replicated and the less trouble they’ll cause over the long term. Sometimes corrections are simple: redesigning a web data input form, for example, might dramatically reduce the number of errors that customers make when filling it out. Other times, identifying the sources of error can be challenging, but it’s always worth investing time and engineering effort into doing so.
No. 2: Do the simplest things first.
Certain data cleansing tasks take far less effort to implement than others. These are always the best candidates for automation. Removing extra spaces, blank cells, improper formatting and duplicate values is relatively straightforward, and should be addressed in the earliest stages of the data cleansing process.
No. 3: Measure data accuracy and monitor errors.
Although it’s possible to ascertain the accuracy of your data through ongoing research, it’s often beneficial to invest in data quality monitoring tools that can handle enterprise-scale data sets and alert your team to the presence of errors – or issues requiring further attention – in real time. Cloud-based solutions that don’t require specialized hardware or administrative overhead are available on a cost-effective subscription basis.
No. 4: Have a steward who takes ownership of the challenge within the enterprise.
In larger enterprises, it’s critical to designate a single individual who can advocate for the importance of data quality within the organization. This person can engage with third-party experts, vendors and the board and C-suite to educate stakeholders on the business value that clean data brings.
No. 5: Leverage pre-built tools, including semantic modeling and machine learning.
Though big data sets are often viewed as valuable because they can be used to train machine learning (ML) and artificial intelligence (AI) algorithms, but ML-based automated solutions also have powerful capabilities for use in data cleansing applications. Algorithms can find duplicate values through clustering, flag possible errors by identifying outliers, and automatically purge records that conflict with other data sets elsewhere in the enterprise.
Though data cleaning demands both time and effort on your team’s part, the benefits that high quality data can bring to the business make it more than worthwhile.
For more information on how Cloudreach can help you prepare your business to harness the power of its data and become more data-led, click here.