Easily Fixable Data Analytics Challenges Faced by Your Business Enterprise

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Easily Fixable Data Analytics Challenges Faced by Your Business Enterprise

Data analytics has become an indispensable part of the business world. Look all around and you will realize that everything is already data-driven. A bigger pool of organizations is moving towards executing this practice on their premises also. However, as per a 2016 report from Gartner, it was discovered that lone 15 percent of the aggregate businesses who attempt to execute data analytics, win the battle, and the rest stall out in the pilot phase of the venture. After running a background check on this problem, it was understood that there is a set of common issues that every one of the firms is confronting. In this article, you will discover the 10 most regular concerns upsetting the execution of data analytics ventures and the approaches to effectively resolve them.

10 Data Analytics Challenges

1. Large Volume of Data to Store

The first and foremost challenge faced by the companies implementing data analytics is associated with data storage and analysis. Higher traffic websites such as the New York Times and Amazon may generate petabyte data or more in a single month. IDC in its Digital Universe reported estimated that the information stored in the IT systems of the world is doubling every two years.

Another issue with all this immense data is that a major chunk of it is in the unstructured form. Documents, videos, audios, and photos are comparatively difficult to search, analyze and occupy a lot of space.

To deal with these data problems, organizations are turning to various types of technologies. Technologies like tiering, compression and deduplication are being utilized to reduce the amount of space required to store the data. To manage the analysis part, firms use tools like Hadoop, NoSQL databases, Spark, BI applications, big data analytics software, ML, and AI to dig out the insights that they want.

Data literacy is the solution to this challenge. Instead of collecting any data available from various sources, enterprises need to work on collecting meaningful data. Hiring data analysts and training employees to understand data literacy will help businesses collect data that is useful for decision-making. 

Another method to overcome the challenge is to scale the data warehouses/ data lakes in stages rather than going for a complete upgrade. This allows enterprises to manage the incoming data without spending billions of dollars at once. 

2. Timely Generation of Insights

The data doesn’t have to be just stored, it has to be used to achieve the business goals. As per the NewVantage Partners Survey, there are some common goals that are shared by almost every organization that deals with data analytics. Some of which include

  • Reducing expenses via operational cost efficiencies
  • Promoting a data-driven culture in the organization
  • Speeding up the process by which services are deployed to the customers
  • Launching products that offer better services to the clients

All these goals when achieved help businesses gain an edge over others in the market. However, the success of which usually depends on how quickly the generated insights are being acted upon. In case, the action time is less the data and insights tend to lose their value in the market. In order to achieve faster speed, some companies are looking forward to using new generation analytics tools and at the same time investing in real-time analytics that will dramatically reduce the time taken to generate reports.

Real-time analytics are ruling the industry, thanks to powerful tools like Tableau, Power BI, Qlik, etc. The best way to generate timely insights is to choose the right tools for data storage and analytics. Where should a business store the data? In-house servers or cloud solutions like Microsoft Azure? Which analytical tools can easily handle big data and deliver real-time results? Talking to an expert will help businesses choose the right tools and customize them for their requirements.

3. Less Understanding of Analytics

Data analytics has the ability to bring in precise and accurate decisions for the organizations that tend to use it. It helps them in managing their finances, launching new products, understanding their customers and much more. However, there is still a lot that needs to be done so that people have a clear picture of data analytics and its importance in today’s world. NewVantage found that only 27% of organizations in 2020 called their businesses data-driven. Moreover, 73% of businesses felt that big data management is an ongoing challenge.

Seminars, small workshops on the office premises, discussions, and real-life examples are some of the ways that organizations are using to improve the understanding of data analytics among their staff. Training and empowering employees is vital to getting desired results from the data-driven model. It’s not sufficient if the top management and C-level executives understand the need for analytics. Every employee in the organization who needs to work with the new tools and systems has to realize the importance of quality data and accurate insights. 

4. Recruiting Skilled Talent

Organizations find it a challenging task to both retain and recruit talent that can handle their data and utilize it to derive useful insights.  The 2017’s Robert Half Technology Salary Guide has suggested huge pay raise for the positions of data scientists and business analysts all over the globe.  Companies are also trying to train their staff to learn some of the tools and techniques that can help them handle their data needs. But, there is still a large gap in the understanding of this field. The trend is continuing even in 2022, with Revenue Cycle Analyst and Database Administrator being the top two positions with the highest pay increase.

Also, there are many firms that solely deal with data analytics and all the related operations. In case, the organizations are unable to find a suitable recruit for their firm, they can consult the professionals and get their data needs satisfied. These data analytics firms have all the expertise that is required to accomplish the given task. As an added advantage, outsourcing the work to another firm proves to be more economical than setting up a whole new section in an already established company. Hiring offshore solution providers and dedicated teams to manage data analytics for the business is a cost-effective solution. 

5. Integrating Disparate Data

With the generation of a large amount of information, data governance is a growing concern for institutions. Usually, the firms set up a group of people to set data governance policies for their enterprise. The main aim of this group is to validate the data and accept its most alleged form. A strategic approach to data integration is a better choice compared to ad hoc integration projects, which can lead to excess rework. Though data lakes have their advantages, they aren’t always the right choice to store disparate data, especially if the enterprise doesn’t have a clear idea of how to process this data and integrate it with the existing systems. 

To understand this better we can take the help of an example. A patient’s address on a hospital record and on the record of a pharmacy from where he recently purchased some medicine is different. Now, this would create confusion as we’ll have to find a way to find out which is the correct address and remove the disparity arising in storing the data. This act can be called data validation and is a challenge faced by many data-driven companies. Data redundancy needs to be managed and for that, some machine learning algorithms or artificial intelligence based technologies can be used.

A centralized data warehouse where data input is streamlined and automated will help get rid of data redundancy and reduce the risk of human error. Automation also saves time and allows employees to focus on the core aspects of their job. 

6. Resistance in the Organization

When there is less number of people who know about a particular field, it becomes very difficult for an organization to start implementing it. In a firm, there may be a number of people who will doubt the efficiency of data analytics and refer to it as a huge waste of money, time and labor. In such scenarios, it becomes difficult for the owners to take a timely decision and make data analytics an integral part of their firm.

It’s natural for employees to not support new technology they don’t understand. Confusion, anxiety, stress, etc., affect productivity and quality of work. The management should first prepare the employees to accept the changes by explaining how the latest tools and technology will make their work easier. Digital transformation includes changing the work culture in the business by assuring employees that this change is good for their job. 

Furthermore, there is a simple methodology that can be applied to resolve this issue. Every firm can appoint a chief data officer who will only supervise the working of this new department and report on the issues and its progress. This will not affect the ongoing hierarchy in the company meanwhile giving time for the new field to flourish and offer its best results. That said, we recommend including employees in every stage of adopting new technology to make the transition smoother. 

7. Data Visualization and Representation 

It’s not enough to analyze data. The reports generated have to be easy to understand and process in a quick time. Gone are the days when employees pored over pages of spreadsheets with two-dozen columns and countless rows. In today’s fast-paced world, data reports need to be available at the fingertips and understood with a single glance. 

Graphs, charts, pies, bars, heatmaps, dot maps, etc., are used to visually represent the data and insights. Of course, these are not manually created by employees. We have business intelligence tools that offer data visualizations and customizable dashboards. Employees only need to access the dashboard to get the required information in real-time before making a decision. The dashboards are user-friendly and have a drag-and-drop facility to add/ remove filters/ elements. Many data analysts recommend using Power BI or Tableau for data visualization.  

8. Budget and Returns 

Any new development or change requires an additional investment for the business. While the cost of digital transformation is too expensive to bear for smaller businesses, not having a proper plan of action is a challenge for large enterprises. Moreover, the top management doesn’t always approve investments that data scientists and analysts think are necessary for the business. The first question the management will ask is about returns. When can the business get returns from the investment? What guarantee does the business have that the investment will be a success? 

Build a team of risk managers to calculate the cost and returns. Gather more information about the technologies before making the final decision. A sure way to optimize expenses and increase returns is by hiring an expert to understand the business requirements and recommend tools and technologies that suit the specifications. It’s important to not let the costs go out of control. 

9. Flexibility and Scalability 

Not all tools are flexible enough to be customized for the business. At the same time, the enterprise may not have enough budget allocation to build the entire infrastructure from scratch. Scaling analytics is another major concern for growing businesses. Data collection, storage, and processing become increasingly complex as the organization grows bigger. More resources are required to derive insights for decision-making. 

Enterprises will find it easier to migrate to the cloud than rely on in-house servers and systems to overcome the challenge. Cloud computing has become famous for being flexible and scalable to match business requirements. Enterprises can lease the necessary tools instead of buying them. Many vendors and cloud service providers offer an on-demand price model where organizations can scale their cloud resources over time. Automation is another way to reduce costs when scaling analytics. 

10. Data Security and Compliance 

Data security continues to be a crucial challenge for many enterprises. Big data collection and storage require strict data governance and compliance standards. There are no margins for error when dealing with sensitive information and insights. In large enterprises, it becomes very difficult to identify the source of the data breach. Remote storage, lack of data governance, IoT devices without proper protection, and human error are common reasons for a lapse in data security. 

This challenge can be tackled by hiring cybersecurity experts to thoroughly inspect the business infrastructure and create a security plan to cover the weak areas. Using big data analytical tools, encrypting data, and training employees periodically to be aware of cybercrimes can help organizations ensure data security.

Every Problem Has a Solution

Data Science is capable of much more than what it is today, but there are few deterrents that it has to cross before reaching its full potential. Once people will understand its importance and how it can make their business profitable and lives simpler, the integration will be friction-free.

If you are among those looking forward to giving your business a boost, implementing data analytics could be the path to walk. Consult data analytics experts who can enlighten you on the decisions that would change the way you do your business.

Like DataToBiz, which offers a platter of premium insights that you might just need, partnering for data science is the call of the future. Right from understanding your issue to modeling it into a data analytics problem and then resolving to offer the optimum results is their niche.

Data Science & Analytics

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