Creating a data-driven culture is not the only challenge that organizations and data professionals must overcome to get value out of the data collected and its analysis. There are many other data management pain points or challenges to overcome. The following are the top 5 challenges for data management and the strategies to overcome them.
Integration
Data management is a process that consolidates data from various sources for meaningful analysis. Organizations can get the complete picture of their business and customers with a robust data integration plan without compromising operations.
Today, many technology implementations and business initiatives are supported by the deployment of data integration solutions. However, to avoid problems in the future, organizations must understand the best ways to deploy data integration solutions. One of the most important things here is having or developing a data integration plan.
Consolidating data from disparate sources into a single system is the primary purpose of any data integration plan. The following are the critical steps to developing a robust data integration plan that can help ensure this.
- Defining the Project
A project must have its objectives clearly defined to ensure its success can be measured and monitored. The project’s parameters must be included in these objectives to define the project scope. Also important is ensuring that all related software, datasets, and databases are listed. Another critical aspect of defining the project is determining whether there is a need for accessing data in real time. If it is not required, you need to find how frequently data needs to be transferred.
- Knowing the systems
A critical step in developing a robust data integration plan is getting a complete view of the business systems and understanding the critical processes involved. This step necessitates collecting any reliable information and documentation that can be accessed
- Determining the Criteria for Data Quality, Validation, Control Policies, and Performance
When developing a data integration plan, it is essential to determine data quality, validation, control policies, and performance criteria. It will ensure a smooth integration by identifying and resolving compatibility and interoperability issues early.
- Defining how data will be processed
Regardless of its form, all data must be profiled to ensure that any issues are identified and that the data meets the requirements. Data must be profiled for dates, monetary values, format, type, length, gaps in the dataset, and more. Also important is considering data quality rules such as validation, cleansing, deduplication, and consolidation.
- Identifying Project Champion or a Dedicated Point of Contact
The fifth and final step in developing a robust data integration plan is identifying a project champion or a dedicated point of contact. This person will monitor progress and remove roadblocks by engaging all right stakeholders efficiently and effectively.
Getting Real-Time Insights
Another data management pain point is getting real-time insights. When we move from analyzing static data to handling inputs in real-time, a great deal of complexity is added to the data management and analytics; it makes having a new range of data analysis tools that can handle data of high velocity and veracity a necessity. These tools include computation, frameworks, ETL engines, libraries, and visualization.
While recent innovations such as fully automated Extract, Transform, and Load (ETL) engines make data scientists’ life easier, there is still a long way to go for organizations looking to use real-time insights.
Business can unlock valuable opportunities by leveraging machine learning for data collection, protection, and analysis. Gartner believes that AI will be a major driver or reinventing both business models and the customer experience through 2025.
Siloed Analytics and Competing Results
Siloed analytics and competing results are a challenge for a company’s data needs because of the following:
Limited information—if one department of an organization has access to certain types of information and other departments don’t, then this could stifle the growth of those departments.
Redundant data—an organization could run into duplicate or redundant data if multiple departments use the same data source but rely on different sources of information.
Interdepartmental inefficiencies—if they rely on different processes or different data markups, individuals in an organization could also face problems in communicating and collaborating with others, leading to more miscommunication and disorganization.
The good news is that data management and reporting platforms like ClicData can help you break down siloes in analytics and avoid competing results by:
- Using the right software, enterprise-wide
- Encouraging more active information
- Blurring departmental descriptions and roles
- Setting company-wide goals and objectives
- Taking note of and proactively correcting incidents
Lack of Skills to Interpret and Apply Analytics in Business Context
Despite the COVID-19 pandemic’s fundamental downward pressure on several industries and its long-term effects, 64.2ZB of data were created or reproduced in 2020. Over the following five years, more digital data will be produced than at any time since digital storage has become widely available. However, not all businesses are geared to benefit from this digital data explosion.
Even if an organization has the required technical staff, the lack of skills to interpret and use analytics among business staff can be another significant barrier to effectively using data and making data-driven decisions.
To make things worse, data scientists and business leaders are hardly on the same page. Additionally, entry-level analysts often deviate from the value of the data for the business and come up with insights that don’t solve any of the organization’s problems. On top of that, they are a limited number of data scientists who can add value to the business.
As a result, many organizations today have started looking for data management and reporting solutions that use automation, machine learning, and artificial intelligence to make sense of data with minimal manual coding.
Data Governance and Security
Companies source data for decision-making from many internal and external data sources. However, the governance of this data is very much an issue. To improve the management of data and systems used for decision-making, organizations need to ensure three things: an agile IT architecture, setting up bodies to drive cross-departmental alignment for data governance and data-driven decision making, and defining and pervasively using KPIs across the organization.
Another data challenge for organizations is ensuring their security and integrity. Due to the criticality of the data, even a minor incident can result in enormous losses for the organization. Some companies are leveraging machine learning technology to avoid cybercrime to overcome this problem.
Final Word
Above are the data management pain points that all organizations eventually realize as they work through more and more data. The most common conclusion that organizations come to is that a comprehensive data management and reporting platform is required.
ClicData offers a data management solution that is easy to implement, easy to use, and allows businesses to collect all the data that they need, as well as do the following:
- Automatically monitor data for completeness, timeliness, and correctness;
- Administer the data so that users can easily access the data they require from a single, central pool of data;
- Automatically normalize the data so that it can be quickly utilized in analysis and other systems;
- Deliver high availability and performance to meet the real-time analytical needs of businesses today.
To learn more about how you can overcome the top data management pain points or data management challenges in 2022, tune into ClicData for all the latest information.
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