1. Solutions do not provide new or timely insights
(1) Insufficient data
Some organizations may not be able to generate new insights due to insufficient analytics data. In this case, a data audit can be performed and ensure that existing data integrations provide the required insights. Integration of new data sources can also eliminate the lack of data. There is also a need to check how the raw data entered the system and ensure that all possible dimensions and metrics are publicly available and analyzed. Finally, the diversity of data storage can also be an issue. This can be addressed by introducing a data lake.
(2) Slow data response
This often happens when organizations need to receive insights in real time, but their systems are designed for batch processing. As a result some data remains unusable right now because it is still being collected or pre-processed.
Examine whether the organization's ETL (Extract, Transform, Load) is able to process data on a more frequent schedule. In some cases, batch-driven solutions can triple schedule adjustments.
(3) Old approaches with new systems
While organizations adopt new systems. But it is difficult to get better answers through the old approach. This is primarily a business problem, and solutions to this problem vary from situation to situation. The best approach is to consult with industry experts who are experienced in analytical methodologies and understand their business areas.
2. Inaccurate analytics
(1) Poor quality of source data
If an organization's system relies on flawed, incorrect, or incomplete data, the results obtained will be poor. Data quality management and a mandatory data validation process that covers every stage of the ETL process can help ensure the quality of incoming data at different levels (syntactic, semantic, business, etc.). It enables organizations to identify and remove errors and ensures that changes to an area show up immediately, resulting in pure and accurate data.
(2) System defects related to data flow
The occurrence of such problems can be reduced by performing high-quality testing and validation of the development lifecycle, thereby minimizing data processing problems. Even with high-quality data, the organization's analytics may provide inaccurate results. In this case, it is necessary to perform a detailed examination of the system and check that the implementation of data processing algorithms is trouble-free
3. Using data analytics in complex environments
(1) Data visualizations show clutter
If the organization's reports are too complex. It is time consuming or difficult to find the necessary information. This problem can be solved by hiring a User Interface (UI)/User Experience (UX) expert, which will help the organization create compelling user interfaces that are easy to navigate and use.
(2) Over-designed systems
Data analytics systems deal with a lot of scenarios and blur the focus by providing organizations with more functionality than they need. This also consumes more hardware resources and increases costs. As a result, users have access to only some of the functionality, some of the other features are somewhat wasteful, and their solutions are overly complex.
Identifying redundant functionality is important for organizations. Enable the organization's teams to define key metrics: what they want to be able to accurately measure and analyze, which features are frequently used, and what the concerns are. Then discard all unnecessary functionality. It's also a good option to have experts in the business area help the organization with data analysis.
4. Long system response times
(1) Inefficient data organization
Perhaps the organization is having a hard time organizing its data. It is better to check whether its data warehouse is designed according to the required use cases and scenarios. If not, a redesign would certainly help.
(2) Big Data Analytics Infrastructure and Resource Utilization Issues
The problem could be with the system itself, meaning that it has reached its scalability limits, or it could be that the organization's hardware infrastructure is no longer adequate.
The simplest solution here is to upgrade, i.e. add more computing resources to the system. This is great as long as it helps improve system response within an affordable budget and as long as the resources are utilized wisely. From a strategic point of view, the smarter approach is to split the system into separate components and scale them independently. However, it is important to remember that this may require a redesign of the system and additional investment.
5. Expensive to maintain
(1) Outdated technology
The best solution for organizations is to adopt new technologies. In the long run, they will not only reduce the maintenance costs of the system, but also improve reliability, availability and scalability. It is also important to undertake system redesigns incrementally and replace old elements with new ones over time.
(2) Not optimal infrastructure
There is always some room for cost optimization in infrastructure. If the organization is still using on-premise facilities, migrating the business to a cloud platform may be a good option. With cloud solutions, organizations can pay as they go, which can significantly reduce costs.
(3) Choosing an over-engineered system
If the organization is not using most of the system functionality, it needs to continue to pay for the infrastructure it is using. Organizations modify business metrics and optimize the system for their needs. Some components can be replaced with simpler versions that are more aligned with business needs.