
Data integration becomes necessary while consolidating data from various sources. During the integration process, many data entry operators tend to overlook crucial elements, unaware that their errors can have significant consequences. Those mistakes can lead to compromising data integrity, security, and usability.
Let’s make it crystal clear which common mistakes must be avoided for insightful data entry.
Common Mistakes in Data Entry while Integration
Mistake 1: Overlooking Metadata Management
Metadata is extremely useful data that helps in finding the actual context in data. Have you ever considered the label on a food item? It shows which ingredients it has, where it originated, its expiry date, and more. These details are like metadata that eliminates the scope of its misuse. Overall, metadata entry introduces the context of a specific piece of data, which eventually discloses its potential use.
Overlooking metadata can also hide data lineage, such as where it is extracted, how it has been altered, and what purpose it serves. Its mismanagement impacts overall analysis and reporting. Ultimately, its misinterpretation can cost a lot, besides misguiding campaigns for sales and marketing. Consider the misinterpreted marketing data, which is used to derive marketing strategies. Poor data entry will certainly lead to flawed campaigns, which means the wastage of supporting resources. Revenue would be impacted in the end because erroneous data won’t introduce you to true opportunities.
How to Solve?
Only proper management is the answer. You may simplify it by using tools like Informatica Meta Data Manager or IBM InfoSphere. Tools like these can automatically segment, access, and even update meta data for seamless records.
Mistake 2: Skipping Data Quality Checks
Integrating data without testing or auditing its quality can infest data with lots of flaws and redundant or incomplete entries. Consider how you merge data from multiple systems. It is considered erroneous data with many duplicate and inconsistent entries. It can lead to misleading analysis and also adversely impact the effectiveness of decisions.
The data-powered insights will be compromised without addressing quality issues upfront. The key business decisions across departments, encompassing HR to sales and finance, eventually mislead because of erroneous insights.
How to Solve?
To get rid of potential loss, a stringent data audit is necessary. This audit for accurate data involves validation, data cleansing, and deduplication, which filter out irrelevant data. For automatic correction and assessment, besides best practices, SAS Data Quality and Trillium tools prove incredible with minimal manual interference.
Mistake 3: Ignoring Data Security and Compliance
The sensitivity of data has generated the need for compliance. As a result, some crucial regulations were formed, like GDPR, CCPA, and HIPAA. Non-compliance can significantly lead to breaches and unbearable penalties. This case indicates that the data does not comply with the fixed global standards and, hence, can cause substantial legal and reputational risks.
How to Solve?
This problem can be addressed by following the best data security practices, like encryption of data in transit and at rest. Also, defining access controls and regular quality checks also help in meeting globally recognized data standards. For effortless and consistent monitoring, deploying data loss prevention (DLP) tools, such as McAfee Total Protection for DLP or Symantec DLP, is helpful. Their deployment automates tracking when it is shared or in transit.
Mistake 4: Selecting Inappropriate Integration Tools
Unsuitable data integration tools can turn the speed of business growth sluggish. This is simply because the data continues to scale over time as the business grows. When its performance and resources mismatch, the performance and efficiency of that business decline. And sometimes, its resources get over-utilized. These both conditions are not good for data processing. When the deployed tool lacks scalability or flexibility, it paralyzes its ability to adapt and innovate. Additionally, incompatible tools with existing systems can paralyze communication failures or discrepancies.
Overall, incompatibility may also hamper effective collaboration. Mismatched tools might cause missed data updates or system malfunctions. So, wisely selecting the right tools is necessary for long-term adaptability.
How to Solve?
For the right tool, you must be crystal clear about objectives. So, selecting platforms or tools that allow to enter a scaling volume of data and are compatible with existing systems is a must. Hands-on testing can also clarify which solutions align with your needs. Apart from it, consider the reputation of the tool vendor so that its support can be accessible in time if required in the future.
Mistake 5: Inconsistent Data Formats and Standards
The integration of data is successful if data entries are consistent in formats. Because the data comes from a variety of platforms and sources, the diversity in its format will be a common thing that you cannot ignore. So, establish a uniform format or ask a highly skilled data entry operator for it before processing. Otherwise, the consolidated data will cause chaos. Its interpretation won’t be easy, as the diversity in formats won’t allow it to sync easily. All in all, data standardization is something you cannot get rid of. If it is underestimated, errors would diminish the usability of that data.
How to Solve?
Inconsistency of data can be battled out by following the rules of data standardization. For it, these protocols must be evolved prior. So, start with defining naming conventions and classification criteria. These protocols should be for everyone to follow among stakeholders. Besides, deploying tools for cataloging or tracking data lineage will help in establishing consistency across all data entries. With that, centralize the metadata repository for minimizing manual effort to find accurate data with greater accuracy and speed.
Conclusion
For sure, data integration is a necessity if you really want to drive insights for informed decisions. Some mistakes can adversely impact the end result. So, avoid neglecting metadata, skipping quality assessments, and overlooking the security of precious data. Besides, choose appropriate tools and enter data while following consistent data standards. These are some keys to unlock actionable insights that really prove their worth when executed well.