Data quality and integrity are essential for any analytical project, as they affect the accuracy, reliability, and validity of the results. Poor data quality and integrity can lead to errors, biases, and inefficiencies that can undermine your work and reputation. How can you ensure data quality and integrity at work? Here are some tips to help you.
Before you collect or use any data, you need to define your data requirements clearly and explicitly. What are the objectives, questions, and hypotheses of your project? What are the data sources, formats, and types that you need? What are the criteria and standards for data quality and integrity that you will follow? By defining your data requirements, you can avoid unnecessary or irrelevant data, and ensure that your data meets your expectations and needs.
Establishing a solid foundation is key before delving into data collection or utilization. This involves precisely outlining your project's objectives, formulating essential questions and hypotheses, specifying required data sources, formats, and types, and setting stringent data quality and integrity criteria. By defining your data requirements, you streamline the process and guarantee the relevance and alignment of the acquired data with your project's goals, avoiding any unnecessary clutter.
Before starting any analytical project or analysis there are some base objectives that is required to get achieved from the report. Right Data collection and screening is vital part for meaning information.
The definition of data requirements is key to ensuring that research processes are well-informed, accurate, and result-driven. Following are some of the aspects - Understand?the objective is crucial - Hypothesis coverage - Desk research, develop understanding of the sector - Questionnaire designing - Data Collection method - Sample planning, define Quota, Geographical coverage to avoid unnecessary irrelevant responses
Defining data requirements, is the most important step in the whole process. I see the applicability of 80:20 principal here. It is that 20% of the whole process which defines the direction to the rest 80% of the process. To achieve the maximum accuracy on this step, Vision and Objective of the project should be clearly defined and understood.
It very important that questions, objectives and hypotheses are clear. For a typical project management, the result chains and intervention guides should be clearly aligned with the logic of the intervention. This is very important in collecting quality data.
Once you have your data, you need to validate and clean it to ensure that it is accurate, consistent, and complete. Validation involves checking your data for errors, such as missing values, duplicates, outliers, or anomalies. Cleaning involves correcting, removing, or replacing any invalid or inaccurate data. You can use various tools and techniques to validate and clean your data, such as data quality software, data profiling, data cleansing, or data transformation.
Two additional points in your data validation: First, have an actual user of the data look at it to confirm that everything is in the correct/expected format. Nothing worse than to discover that there has been a manual process that had been followed for months/years that moved data into Excel for cleanup, and then exported into the database with a critical field (or two) having been changed from numeric to scientific notation. Second, confirm the 'freshness' of the data for the timeframe in question. If records were entered manually up until a certain date, and then the process changed to be updated automatically, verify that the new process is touching every record, because if not - there can be the potential for errors.
Making sure the data we use at work is accurate and trustworthy involves using a bunch of smart strategies. One cool way is to use special tools that help keep the data in good shape. Imagine using software that looks through the data and spots things that don't fit, like missing info or weird numbers. These tools can catch these odd things automatically, so we can fix them right away. Also, there's this trick called data validation rules, where we set rules for the data to follow, like making sure dates are in the right format or numbers aren't too crazy. It's like making sure only the right pieces of the puzzle fit. All these tricks together make sure our data is solid and dependable as time goes on.
Though, tools are there to check for missing, formatting..etc however there is no tool to identify deliberate manipulation of data to show rosy picture or limited & predefine result showcasing. For that, you must understand the process and its nuances to be able to identify manual interventions in the data.
When it's not a one time activity, but a continuous effort, make sure that the validation and in best case the cleaning is well is automated, so you grant integrity and will always produce the same result.
One key step to consider is to recheck input data to actual records/notes. This will help to minimize errors. This might involve a meeting in situations where data collectors are different from those who input records. These two teams need to work together. In other instance a quality assurance staff /Monitoring and result measurement official would help to validate data
Another way to ensure data quality and integrity is to document and standardize your data. Documentation involves creating and maintaining a record of your data sources, definitions, methods, processes, and changes. Standardization involves applying consistent rules and formats to your data, such as naming conventions, units, codes, or categories. By documenting and standardizing your data, you can improve its readability, usability, and traceability, and avoid confusion or misinterpretation.
Furthermore, these practices facilitate better collaboration among teams. When data is documented and standardized, it eliminates guesswork and ensures that everyone is on the same page, enhancing efficiency. It becomes easier to integrate new datasets, troubleshoot issues, and ensure continuity leading to a data-driven culture.
In my experience, standardisation of templates and processes always help any organisation i.e., from an AI/ ML to hardcore manufacturing ones to sustain and build ensuring zero error policy framework. However, the efficiency and effectiveness of implementation depends on stakeholder buy-ins of an organisation. To keep it short it is always beneficial to keep the data in specific formats to export/ import/ digitalise in the future Analytics usage to hasten the decision making process. As recommended in PMI methodology for any projects where multiple and mega documentations being generated/ maintained/ and stored it is suggested to tailor the templates as per organisational efficiency, growth appetite and sustainable practices.
This is the part what do not like the most during analytics and number crunching. Documenting and standardizing your data is the linchpin of effective analysis. but, we need to clearly define data definitions, formats, and structures. This practice fosters consistency, streamlines collaboration, and empowers others to interpret and utilize the data accurately.
One thing I have found usefull is to check the resources of the data, find out if the data comes from the most updated resource.
Ensuring data quality and integrity at work is essential for making informed decisions, maintaining accuracy, and building trust in your analyses. Data Governance and Policies, Develop procedures for data entry, validation, transformation, and storage as well as quality assurance framework.
Data quality and integrity also depend on how you secure and backup your data. Security involves protecting your data from unauthorized access, modification, or deletion, such as by using encryption, passwords, or access control. Backup involves creating and storing copies of your data in a safe and separate location, such as by using cloud storage, external drives, or backup software. By securing and backing up your data, you can prevent data loss, damage, or breach, and ensure its availability and recoverability.
Backing up strategy should align with the volume and criticality of the data. Back up at a Single place like USB Drive and back up in 3 different places in Harddrive, Local Server and Cloud Storage has completely different attributes of confidentiality, integrity and volume of data being handled.
Furthermore, securing and backing up your data are not only good practices, but also necessary steps to ensure the validity and reliability of your retrieval or outputs. Securing and backing up your data can also help you comply with ethical and legal standards, such as data privacy and protection laws. However, these tasks should be compliant with company's policy, risk appetite and budget.
One thing I've found helpful is the importance of backing up the data. In cases when you're setting up a new procedure, the backup process is extremely useful. At this level, we might not know what we need exactly, and in this case if any data loss or security issues occur, the entire new procedure could face heavy data damages.
Finally, you need to review and update your data regularly to ensure that it is current, relevant, and accurate. Review involves evaluating your data for its quality and integrity, such as by using data quality metrics, audits, or feedback. Update involves modifying or adding new data to reflect changes in your project, data sources, or requirements. By reviewing and updating your data, you can identify and address any data quality and integrity issues, and ensure that your data is fit for purpose.
An alternative approach to data review involves conducting a variance analysis against the forecasts. A significant variance in the forecasts can lead to two potential scenarios. Firstly, the variance might be explained by the actual circumstances. For instance, if the sales for certain products for the month are 50% lower than the forecast, this could be attributed to the products having some issues. Alternatively, the variance could arise due to inaccuracies in the data. For instance, the product codes for some sales may be missing in the system. In such instances, revising the data within the system becomes necessary, and then the data will need to be updated and reviewed again.
if the entity has a solid single source of truth for the data needed then this will minimise any shortfalls that may have come from data quality and integrity. However, if it is not clear or not feasible to have a single source of truth for the data needed, then all stakeholders in the project should be aware and accept the alternative sources for the needed data with the some tolerance to data quality and integrity.
Data privacy and data management policies ought to be in place and well communicated in order for employees to know and understand how to obtain, use and dispose off data.
There is a very important point not mentioned is of behaviorial baises . we need to identify if there are any baises in collecting and reviewing the data . For example, Confirmation baises , which is looking for data only that we want to rely upon and ignore the others. For instance , I am researching a particular stock and subconsciously I want the company should do well, then I will try to find and highlight all information favorable to the stock Rest all points are are valid
Foster collaboration between different departments, teams, or key players that handle data. The cross-functional collaboration can lead to more accurate and reliable data.
A good system is also crucial for data integrity. At times, a system might lack proper audit tracing and data locking capabilities, leading to potential issues. For instance, consider a scenario where a $1,000/month order was booked in June. In August, the order gets canceled, and the system simply removes this order from the revenue data output. Consequently, the revenue figure retrieved for June in June would differ from that extracted in August for June, complicating data validation. Systems of this nature should be avoided, even if the costs are low.
As a team, we often observe that there is no perfect environment for gold standard data, in a business at scale with constantly changing demand and supply market. There is value in using good enough data models, with the perspective of someone or some people who understand these dynamics, to arrive at a viable narrative to fulfill business needs and keep moving.