As enterprises grow at a breakneck pace, the volume and velocity of the data generated have increased exponentially. Companies collect more and more data about their customers and internal operations for business intelligence and the analytical insights that this data can offer. However, the biggest challenge for enterprises in leveraging this data to its fullest potential is the quality of the data. To determine the usefulness of your enterprise data, you must evaluate it against these six dimensions:
- Completeness: Is your data missing any values?
- Conformity: Is your data using uniform formats and units across all enterprise systems?
- Consistency: Is your data across systems comparable, or are there instances of conflicting data?
- Accuracy: Is your enterprise data free of errors?
- Timeliness: Is the data available when it is needed?
- Integrity: Can every data record be linked into the dataset correctly?
There can be several reasons for low data quality. Common ones are disparate IT systems across different departments, outdated legacy systems, manual data entry, and lax input validations. If your enterprise data suffer quality issues, opting for data cleansing before inputting it for reporting and analytics is prudent.
What are the components of data cleansing?
Data cleansing is the process of converting raw data into high-quality data through:
- Deduplication: Removal of duplicated data records to ensure a single version of each record
- Removing irrelevant data: Eliminating data that is not relevant to the report or analytics model at hand
- Filtering outliers: Handling data that significantly deviates from the dataset and can skew results
- Filling missing data: Populating empty fields with null values or dummy values based on business requirements
- Fixing formatting issues: Fixing naming conventions, syntax, units of measurement, etc.
- Quality checks: Validating business rules, deleting or replacing inaccurate data
How does data cleansing help make better decisions?
Data cleansing boosts the effectiveness of data for decision-making across every department in an enterprise, irrespective of the size or industry vertical it operates.
- Better marketing choices
Data cleansing ensures you have the latest deduplicated data of your customer base. It allows you to use campaign management tools to create granular customer segments and tailor your offerings and marketing communications to them. Data cleansing also ensures better communication delivery with error-free contact details like email IDs and phone numbers.
- Accurate and timely reporting
Even the best business intelligence (BI) platforms are susceptible to errors if the input data is low quality—garbage in, garbage out. Data cleansing ensures that enterprise reporting is timely and accurate. It enables top management to make strategic decisions based on the latest customer and operational data. Cleansing is crucial for modern organizations increasingly relying on unstructured, user-generated data for business intelligence.
- Spot on market research
Enterprises use market research surveys and competition data for strategic decisions such as launching new products, entering new markets, and designing sale offers. These surveys are collected across various offline and online touchpoints and handled through various third-party data cleansing services. Data cleansing ensures the survey data is consistently formatted, filtered for outliers, and has no null values. This high-quality data is fit to generate compelling visualizations for C-level executives to drive strategy.
- Reliable cashflow predictions
Sales data cleansing is essential for enterprises that spread across geographies, rely heavily on data entry by field sales executives, and record sales in multiple currencies. Clean data enables updated and accurate sales pipeline reports that helps management to track and predict expected cashflows. It drives strategic financial decisions such as capacity expansion, fundraising, working capital management, and debt repayment.
- Unbiased HR decisions
Modern enterprises strive to build a diverse and inclusive workforce. They leverage blind screening techniques. This includes removing data specific to a community, age group, sex, or other irrelevant parameters from applicant resumes using AI-based data cleansing techniques to fight recruiter bias. Standardized score data from technical assessments and pre-recruitment personality tests help HR teams evaluate candidates fairly and drive a data-backed talent strategy.
While some enterprises choose to conduct data cleansing using in-house resources, most modern organizations handling a significant amount of data prefer specialized forms processing services.
Inhouse v/s Outsourced Data Cleansing
In-house data scientists must spend time generating actionable insights from high-quality data rather than in tedious data cleaning activities.
- Outsourcing provides access to skilled data cleaning specialists and cutting-edge tools that boost the efficiency of the data cleaning process.
- Established service providers also conduct industry-standard quality assurance checks to ensure the cleaned data matches the business requirements.
- Outsourcing increases the speed of data cleaning as partners can work outside the enterprise’s time zones and deliver the cleaned data per strict service level agreements (SLAs).
- Secure data transfer methods and stringent non-disclosure agreements (NDAs) ensure data confidentiality.
- Of course, outsourcing helps save data cleansing costs because of economies of scale, better technology, and industry best practices.
Summing Up
If the data cleansing output by your outsourcing provider is up to the mark, you can also engage the same partner for downstream activities such as data mining and analytics. It will ensure a seamless setup for your enterprise reporting function.
