Pain Points and barriers to the adoption of Data services

Companies are realising the strategic importance of data, but one of the biggest problems they face is defining their data strategy.

Most organizations have traditional analytics in place, but the key to their success is to embrace more advanced, predictive, and prescriptive analytics . Here’s where the main barriers are for businesses in moving to a modern data strategy and how to overcome them.  

Main barriers to data contracting

Organizations that have already seen the potential of adopting data-driven projects and approaches are choosing to invest for the benefits it brings, such as improved decision-making, enhanced capabilities, improved operational efficiencies, agility, product customization, and more.

Data consolidation remains a major challenge, as many companies have their information scattered and dispersed across multiple repositories or trapped in inaccessible silos.

With a constantly changing big data-led landscape, organizations are being forced to adopt integrated solutions to help them with data governance.  

As data continues to grow, and at an ever-increasing rate, businesses are faced with the dilemma of managing massive data sets in a cost-effective way. It is no longer a matter of accumulating all available information, but of strategically selecting the relevant data. The keys in this process are to find an efficient selection of data, and discard irrelevant or duplicate data to extract information that is truly meaningful.

In addition, understanding the origin and context of the data is another important and challenging issue. Companies have realized the need for data visualization tools to improve understanding. Knowing how to interpret data ensures more accurate and actionable information.

Data cleansing

Raw data cleansing has become increasingly important as it helps to improve the quality of raw data, as well as ensure the accuracy and reliability of analytics.

The latter is crucial at a time when AI and ML are so pervasive, where data quality directly affects the performance of algorithms.  

GPU and complexity of programming

GPU integration has become a solution for achieving high processing power. However, the challenge lies in simplifying programming.

Solving this technical complexity is crucial to realizing the full potential of data analytics, making GPUs more accessible and cost-effective than CPUs.  

Real-time Analysis and stream processing

At this point in time, real-time analytics is a mainstay for businesses. To this end, we already have technologies and tools that help organizations process data as it arrives. 

Scalability and Challenges in Infrastructure

The interplay between storage and processing in data analytics is critical, and one of the main issues is scalability. This requires modern solutions for dynamic resource allocation.

The ideal system must deploy processing resources according to real-time needs, which is a major coding challenge.  

Data security

With the growth of data from various sources and the increase in cyber-attacks, security risks are growing. Ensuring data security is another key pillar for enterprises.

Authentication solutions are evolving, but there are still many challenges in creating a unified mechanism to address security complexities, especially in the cloud environment.  

Addressing Pain Points in Data Adoption

Addressing the main pain points related to data strategies involves implementing effective solutions and practical improvements.

Some of the most recommended approaches to address each barrier include: 

• Accuracy and completeness of data: 

• Data validation processes and automated checks can be put in place to ensure the accuracy and completeness of data.Establish standardized data collection procedures and train staff in data protocols.Regularly audit and validate data to identify and rectify inaccuracies.Invest in data quality management tools to monitor and maintain data integrity.  

• Data integration: 

• Implement an integrated data management system or enterprise resource planning (ERP) system to consolidate data from various sources.Establish data integration protocols and data exchange formats that allow for a smooth data flow.Leverage available APIs to connect different systems and facilitate data integration.Collaborate with partners and suppliers to establish sharing agreements and streamline information exchange. 

• Data analysis and interpretation: 

• Use advanced analytics tools and techniques such as data mining, machine learning, and predictive analytics to extract meaningful insights from feedback data.Invest in data visualization tools to present data in an intuitive and user-friendly way.Develop data analytics capabilities within the organization by providing training and resources to data analysts and teams.Foster a data-driven culture that promotes the use of data in decision-making.  

• Real-time visibility: 

• Implement real-time tracking systems, such as IoT-enabled devices and RFID technology, to monitor the status and location of returned equipment.Integrate tracking data into centralized platforms or dashboards for easy access and visibility.Leverage cloud technology to enable real-time data sharing and collaboration. 

• Organization across the company: 

• Establish cross-functional teams or committees to analyze data and transform knowledge into practical strategies.Develop a process for prioritizing and implementing data-driven improvement initiatives.Foster cross-departmental communication and collaboration.Monitor the progress and impact of implemented initiatives, to change and refine them based on results.  

Business Data Strategy

By applying the best practices and solutions mentioned above, companies can effectively address data-related pain points by looking at them holistically and applying a comprehensive approach that integrates data management, analytics, and process improvements.

At Plain Concepts we help you to formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analytics services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can shape your decisions and drive your business forward.