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16 min read
In this article, we will go through the most typical big data analytics issues, investigate possible root causes, and highlight the potential solutions to those. Unfortunately, in some cases any fixes are quite expensive to implement once the system is already up and running. It is better to think smart from the very beginning when your big data analytics system is yet at the concept stage. Thus, will also share suggestions on what one should pay attention to when implementing a big data analytics platform from scratch.
Business analytics solution fails to provide new or timely insights
Using data analytics is complicated
Useful tips to consider when building a Data Analytics system
How Sigma Software can help you in implementing real time big data analytics
In today’s digital world, companies embrace big data business analytics to improve decision-making, increase accountability, raise productivity, make better predictions, monitor performance, and gain a competitive advantage.
However, many organizations have problems using business intelligence analytics on a strategic level. According to Gartner, 87% of companies have low BI (business intelligence) and analytics maturity, lacking data guidance and support. The problems with business data analysis are not only related to analytics by itself, but can also be caused by deep system or infrastructure problems.
Let’s dig deeper to see what those problems are and how those may be fixed.
So then, you have invested into an analytics solution striving to get non-trivial insights that would help you take smarter business decisions. But at times it seems, the insights your new system provides are of the same level and quality as the ones you had before. This issue can be addressed through the lens of either business or technology depending on the root cause.
| Root cause | Description | Recommendations |
| Lack of data | Your analytics does not have enough data to generate new insights. This may either be caused by the lack of data integrations or poor data organization. | In this case, it makes sense to run a data audit and ensure that existing data integrations can provide the required insights. Sometimes, integration of new data sources can eliminate the lack of data. In some cases, data might be present inside the solution but not be accessible for analytics, because your data is not organized properly. It is worth checking how raw data comes into the system and make sure that all possible dimensions and metrics are exposed. Another common issue is data storage diversity – data might be hosted within multiple departments and data storages. Therefore, direct access to it might be inefficient or even impossible. One can cope with this issue by introducing a Data Lake (centralized place where all important analytical data flows settle and are tailored with respect to your analytics needs). |
| Long data response | The data lags behind the speed, at which you require new insights. This usually happens when you need to receive insights in real- or near-real-time, but your system is designed for batch processing. This means that the data you need here and now is not yet available as it is still being collected or pre-processed. Don’t confuse long data response with long system response. These are different concepts (we’ll deal with the latter further down the article). | As a rule, it is way too difficult to adapt a system designed for batch processing to support real time big data analysis. We recommend checking if your ETL (Extract, Transform, Load) is able to process data based on a more frequent schedule. In certain cases, batch-driven solutions allow schedule adjustments with a 2 times boost (meaning you may get the data twice as fast). There is another option that might help. It is an architecture approach called Lambda Architecture that allows you to combine the traditional batch pipeline with a fast real-time stream. The approach might extend the existing batch-driven solution with other data pipelines running in parallel and processing data in near-real-time mode. Lambda architecture usually means higher infrastructure costs. However, it also brings additional benefits like better system and data availability. |
| Old approaches applied to a new system | You have transferred your typical reports to the new system. However, it would be extremely difficult to get new answers, if you ask old questions, even with a powerful system. | This is rather a business issue, and possible solutions to this problem differ a lot case-by-case. One can unlock new insights by fine-tuning the analysis logics (e.g. investigating other data interdependencies, changing reporting periods, adjusting data analysis angle). The adjustments that you may need are way too diverse. One general piece of advice we can give is simple. Consult a subject matter expert, who has broad experience in analytical approaches and knows your business domain. |
Nothing is more deleterious to a business than inaccurate analytics. At first, the insights may seem credible, but eventually, you notice that these insights are leading in the wrong direction. This is a serious issue that needs to be addressed as soon as possible.
| Root cause | Description | Recommendations |
| Poor quality of source data | Your analytics can generate poor quality results, if the system relies on the data that has defects, errors, or are distorted and incomplete. | Data quality management and an obligatory data validation process covering every stage of your ETL process can help ensure the quality of incoming data at different levels (syntactic, semantic, grammatical, business, etc.) It will enable you to identify and weed out the errors and guarantee that a modification in one area immediately shows itself across the board, making data pure and accurate. NB! Sometimes poor raw data quality is inevitable and then it is a matter of finding a way for the system to work with it. |
| System defects related to the data flow | This happens when the requirements of the system are omitted or not fully met due to human error intervention in the development, testing, or verification processes. | High-quality testing and verification of the development lifecycle (coding, testing, deployment, delivery) significantly reduces the number of such problems, which in turn minimizes data processing problems. So, if your analytics provides inaccurate results even when working with high-quality data, it makes sense to run a detailed review of your system and check if the implementation of data processing algorithms is fault-free. |
The next problem may bring all the efforts invested in creating an efficient solution to naught. If using data analytics becomes too complicated, you may find it difficult to extract value from your data. The complexity issue usually boils down either to the UX (when it’s difficult for users to navigate the system and grasp info from its reports) or to technical aspects (when the system is over-engineered). Let’s get this sorted out.
| Root cause | Description | Recommendations |
| Messy data visualization | Your users get lost in the reports and complain it is time-consuming or next to impossible to find the necessary info. This issue is rather a matter of the analytics complexity your users are accustomed to. If you have encountered this issue, there is a chance that the level of complexity of the reports is too high. | This can easily be fixed by engaging a UX specialist, who would interview the end-users and define the most intuitive way to present the data. Data visualization tools like Klipfolio, Tableau, and Microsoft Power BI can help you create a compelling user interface that is easy to navigate, creates necessary dashboards and charts, and provides a flexible and robust tool to present and share insights. It may also be a good idea to create separate reports for business users and your analysts, thus providing the former with simplified reports and giving the latter more details presented in a more complex way. |
| The system is overengineered | The system processes more scenarios and gives you more features than you need thus blurring the focus. That aside, it also consumes more hardware resources and increases your costs. As a result, users utilize only a part of the functionality, the rest hangs like dead weight and it seems that the solution is too complicated. | As a rule, it is a matter of identifying excessive functionality. Get your team together (a product manager, a business analyst, a data engineer, a data scientist, etc.) and define metrics: what exactly you want to measure and analyze, what functionality is frequently used, and what is your focus. Then check the possibility to get rid of all unnecessary things. The task may turn out to be not as trivial as it seems. So, involving an external expert from your business domain to help you with data analysis may be a very good option. |
The next problem is the system taking too much time to analyze the data even though the input data is already available, and the report is needed now. It may not be so critical for batch processing (though still causing certain frustration), but for real-time systems such delay can cost a pretty penny.
| Root Cause | Description | Recommendations |
| Inefficient data organization | Perhaps the data in your data warehouse is organized in a way that makes it very difficult to work with. For example, you have excessive usage of raw non-aggregated data. | It is better to check whether your data warehouse is designed according to the use cases and scenarios you need. In case it is not, re-engineering will definitely help. For example, if you have a lot of raw data, it makes sense to add data pre-processing and optimize data pipelines. |
| Problems with big data analytics infrastructure and resource utilization | The problem can be either in the system itself, meaning it has reached its scalability limit, or your hardware infrastructure may be no longer sufficient. | In most cases, the simplest solution is upscaling, i.e. adding more computing resources to your system. It is not always the optimal solution, but might save the day for a while. It is good as long as it helps improve the system response within an affordable budget, and as long as the resources are utilized properly. A wiser approach from a strategic viewpoint would be to split the system into separate components and scale them independently. However, this may require additional investments into system re-engineering. |
Any system requires ongoing investment in its maintenance and infrastructure. Certainly, every business owner would like to minimize these investments. Thus, even if you are happy with the cost of maintenance and infrastructure, it is always a good idea to take a fresh look at your system and make sure you are not overpaying.
| Root Cause | Description | Recommendations |
| Outdated technologies | New technologies that can process more data volumes in a faster and cheaper way emerge every day. Therefore, sooner or later the technologies your analytics is based on will become outdated, require more hardware resources, and become more expensive to maintain, than the modern ones. Furthermore, it is more difficult to find specialists willing to develop and support solutions based on legacy technologies. | The best solution is to move to new technologies, as in the long run, they will not only make the system cheaper to maintain but also increase reliability, availability, and scalability. It’s better to perform a system redesign step-by-step gradually substituting old elements with the new ones. If you do not yet use a microservice approach, it may also be a good idea to introduce it and upgrade both your system architecture and the tech stack you use. |
| Non-optimal infrastructure | Infrastructure is the cost component that always has room for optimization.
| If you are still on-premise, migration to the cloud might be a good option. With a cloud solution, you pay-as-you-use significantly reducing costs. If you have any restrictions related to security, you can still migrate to a private cloud. If you are already on the cloud, check whether you use it efficiently and make sure you have implemented all the best practices to cut the spending. |
| The system that you have chosen is overengineered | If you do not use most of the system capabilities, you continue to pay for the infrastructure it utilizes. | Revising business metrics (requirements, expectations, etc.) and optimizing the system according to your needs can help. You can replace some components with simpler versions that better match your business requirements. During the design part, it is important not to get carried away with the optimization rush, as you can face cross-cutting changes when the cost of implementation grows higher than the savings you will get. |
As you can see, adjusting an existing business analytics platform is possible, but can turn into a quite challenging task. If you miss something at the new solution design & implementation, it can result in a loss of time and money.
If you haven’t built your big data analytics platform yet, but plan to do it in future, here are some tips on how to build the big data analytics solution with the maximum benefit for your business. These recommendations will help you avoid most of the above-mentioned problems.
The better you understand your needs, restrictions, and expectations at the start of a project, the more likely you are to get exactly what you need in consequence. Without a big data analytics strategy in place, the process of gathering information and generating reports can easily go awry.
Before embarking on a data analytics implementation, it’s significant to determine the scenarios that are valuable to your organization. Removing irrelevant data will simplify your visualizations and enable you to focus on relevant scenarios to make the right decisions. Thus, you need to identify:
It is very important to be realistic rather than ambitious while building your business analytics strategy. This way, you can avoid investing thousands of dollars into a complex business analytics solution only to figure out that you need much less than that. Here are the aspects worth considering before implementing your analytics:
Verify that you have defined all constraints from business and SLA, so that later you don’t have to make too many compromises or face the need to re-engineer your solution.
BI tools support a superior user experience with visualization, real-time analytics, and interactive reporting. Embedded BI removes the necessity for end-users to jump from the application they are working on into a separate analytics application to get business intelligence insights.
Not all analytics systems are flexible enough to be embedded anywhere. Therefore, at the design stage, it is crucial to decide where and how you want to embed your analytics, to make sure that the system you choose will allow you to do this without any extra effort. Make sure to choose the right BI tool that can be easily integrated with your dashboard.
Think strategically and ask yourself why you need a BI tool. If you need it only for dashboards and this is not likely to change in future, then you can choose simpler and cheaper dashboard tools. For cases when you need flexible reporting, it is worth considering full-fledged BI tools that will introduce a certain pattern and discipline of working with reports.
The data in your analytics system most likely has different levels of confidentiality. At the very beginning, it’s quite important to define roles and responsibilities according to data governance policies. Secure data access will help you prevent data breaches, which can be extremely expensive and damage your company’s reputation.
Last but not least, make sure your data analytics has good UX. It all depends on who will work with this analytics and what data presentation format they are used to. Well-organized data visualizations significantly shorten the amount of time it takes for your team to process data and access valuable insights. Look for a solution that can allow you to create appealing tables, graphs, maps, infographics to deliver a great user experience while still being intuitive enough for less technical users.
To sum up, we would like to say that the major purpose of any analytics system is to breathe life into your data and turn it into seasoned advisors supporting you in your daily business. The brief outline of potential issues, possible solutions and hints we initially wanted to share turned into a long longread. After you have gone this far with the article you may start thinking it is way too complicated, tricky, and challenging to get the right system in place.
In fact, it is not as hard. It is mainly about defining what you need. With all the diversity of solutions available on the market and suppliers willing to help you, we are sure, you will manage it. Remember – long way to Fuji starts with the first step.
We have been implementing big data analytics system of various complexity for more than 15 years. The last 7 years we have been using Big Data technologies.
You can read more about our experience here.
We not only develop and maintain such systems, but also consult our clients on best practices for big data analytics. If you have any questions about implementing analytics and working with Big Data – Contact us.
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Sigma Software provides IT services to enterprises, software product houses, and startups. Working since 2002, we have build deep domain knowledge in AdTech, automotive, aviation, gaming industry, telecom, e-learning, FinTech, PropTech.We constantly work to enrich our expertise with machine learning, cybersecurity, AR/VR, IoT, and other technologies. Here we share insights into tech news, software engineering tips, business methods, and company life.
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