Most businesses deal with large volumes of data, and this increases their workload tremendously. As the volumes of data get bigger, it becomes very challenging to work with, analyze or even back it up. The way a business handles data reflects its ability for management efficiency.
Even with the best data handling practices, businesses face various data-related challenges in their day-to-day activities. This is why businesses need to adopt an effective strategy that can streamline the handling of data. Below are common data-related challenges for businesses.
Comparing data from different locations and sources
Lots of businesses face challenges with comparing data from different locations and sources when handling large volumes of data. This is not only from the technological point of view but from a business performance perspective.
Due to this, most businesses are built simply on bits of joined-up information. To get better insights from data, businesses need to integrate data enabling operations to handle data from disparate sources.
For businesses that cover large geographical areas, there is a need to invest in better tools to help draw better insights from the collected data. Businesses should invest in the best caching solutions that suit their needs to handle the data effectively.
Non-conforming data
Instances of non-conforming data include invoice discrepancies. Just imagine having discrepancies in invoices worth millions! In this case, the payments will not only be put on hold but the company operations will also be impaired until the issue is resolved.
A notable discrepancy with one of the clients is a clear indication that there might be other similar cases, and the business data analysts have to carry out a thorough investigation. Any case of non-conforming data breeds a lot of challenges in drawing insights from the data. Sometimes the purchase and the ledger departments will be forced to sit down for long hours to compare the data and reconcile every instance of data discrepancies.
Append the information from the source data to help track all the processes and actions. Also, designing consistency checks and appending the final results of the data can help to sort these challenges. Invest in great data analytics for businesses to help visualize the relationship between the data patterns and predict how the data will appear.
Limited or no metrics
Limited or no metrics mean no information on the internal and external KPIs. This happens mostly if the business has grown through different methods. It can be a mix of organic and acquired growth, leading to huge confusion where managers cannot tell exactly what’s going on with the inputs and outputs of the business.
How are the different divisions performing? Are the suppliers performing as expected? Without proper metrics, it can be very hard to determine the business’s overall performance. Many times, the managers will be unaware of their downtime and what has caused it. Their targets are just mere guesses to attract more customers to buy their products and services.
If the customers realize this, the business will be forced to compensate them. Most businesses will just provide branded access to capabilities purchased from disparate sources. In an attempt to integrate the two well, the business will be faced with challenges of a lack of combined logging to the supply and resources simultaneously.
Delayed data access
Delayed access to data breeds outdated reports and can no longer be used for business planning. The summary of the reports that will be presented later during the business meeting will not even be close to real-time due to the long drift.
More often, the business is likely to be forced to expand the workforce every month to assemble the data and build summary reports. This will ultimately lead to guesswork and decisions based on outdated information. The best way to relieve the pain felt by delayed access to the data is to develop a report into the storage.
Instead of collecting data and then directly taking it away to make analysis, make proper reporting a store feature. It will help a lot in delivering accurate reports on time.
Poor understanding of customers, preferences and habits
To understand your customers effectively, you need better data analysis methods that will lead to a better understanding of the customers, preferences and habits. Businesses that use websites to sell and communicate with customers have a lot of information at their disposal. So, why do these businesses face challenges with handling the data?
The main challenge is that these businesses rarely can analyze this data, and if they manage to, they are not sure how all the analyzed information can be used. Businesses can use several data analytics tools to capture customer usage, viewing habits, click-throughs, and preferences.
Knowing what your customers like can give a profile of many things about them. The sales team can make intelligent guesses on the customers’ views about different products. It will also be easier to predict the spending habits of the customers. Hence, connecting with the customers for a better business experience will be very easy.
Lack of factual consistency
Businesses often face challenges with factual consistency due to the different data sources and using outdated data for analysis. There is no good excuse for sharing outdated analysis and conflicting information during business meetings, but this often happens to businesses handling large volumes of data.
The document-sharing applications magnify the problems related to the data if they have no inbuilt structure to handle data complexities. There will be too many duplicates of the same document. If the document is updated, the different versions start to breed very fast.
These multiple versions are then spawned and placed in disparate repositories. Lack of factual consistency with data can be prevented at the data entry level and storage by creating documents dynamically from the data store. It will ensure that the latest information is in the document.
Conclusion
Numerous data challenges face businesses. These challenges often happen when the business handles large volumes of data, and a mistake can happen in the entry-level or analysis stage. Businesses can use the practices mentioned in each of the challenges to minimize these challenges or thwart them completely.
