Data is available in abundance, but data acquisition remains a challenge. There are internal data generated from day-to-day business activities, website, customer interaction, CRM records, billing, etc. Then you have third-party data from external sources like data from market research agencies, purchased databases or data collected from competitor’s websites, data from social media, data from search engine results, and so on.

The amount of available data is amazing; the customer information is like a treasure trove. But the challenge for B2B data aggregators is to upgrade the skills and expertise to be able to acquire the data and draw insights out of it. Understanding the challenges will help draw a business plan for data acquisition.

What are the biggest data acquisition challenges?

It is not only about a large amount of data to be captured from multiple sources; speed, relevance, and accuracy are equally important in building an effective B2B database. Companies are required to fine-tune their legacy systems, have flexible budget options, upgrade technology, have efficient data integration systems in place, etc. to efficiently manage data acquisition.

Let’s find out more about these data acquisition challenges and how organizations can overcome them.

1. Building a constantly evolving and adaptive infrastructure

Technology is evolving at a very fast pace and not being able to keep up with tech trends will be a major hindrance in your data acquisition project. With changing trends, your data acquisition strategy will need a change too. You will need an adaptive infrastructure that can be enhanced, replaced, or upgraded based on current data acquisition and management needs.

You need to have a skilled team specially for this purpose who eye market trends quickly and develop solutions even quicker.

• Keep evolving IT infrastructure
• Have adaptive network infrastructure
• Go for high bandwidth connectivity
• Proxies to allow gather data from any geographical location
• Develop parsing tools to make information useful
• Develop a robust storage and processing infrastructure
• Design solutions for future upgrades without affecting any functionality

2. Ensuring resource knowledge and skill uniformity

For a consistent and effective flow of data, you need to build a team of strong, dedicated, and trained data acquisition team. You will need experts in Machine Learning (ML), Artificial Intelligence (AI), Robotic Process Automation (RPA), and similar emerging technologies; to be able to capitalize on data wealth can be a big issue. Without skills, it is a tough job to acquire and draw insights from data.

• Constantly upgrading your current team
• Keep transferring skills to the current team
• Increase human capital with relevant skills
• Include team training costs in your budget while upgrading
• Outsource for specialized skills if not found in-house

3. Managing the dynamic budget allocation

When it comes to budget allocation for data acquisition it becomes a tricky situation. With technology advancing at a fast pace, keeping pace with required infrastructure and keeping the team skill-trained is a challenge. Allocating funds for every project can play havoc with the allocated budget. You upgrade your system one day and the next day it would require further updates. Managing this dynamic budget is a challenge. Even if you plan resources and address the costs in the early planning stage you can never be sure of the expense you would incur.

• Plan your budget well
• Keep buffer for sudden technology advancement
• Allocate funds in advance for skill training
• Look for new revenue options to offset the infrastructure investment
• Outsource your data acquisition to a skilled team with the required infrastructure in place

4. Scaling to client's requirements

With the increase in the volume of data, databases of data aggregators get flooded making storing and processing the data a challenge. Data scaling is important to manage this huge overflow of data and any potential growth in data requirements.

Any scalability issue like high CPU usage, high disk usage, or low memory, if not managed can impact the workflow negatively. Take appropriate measures before your problem starts affecting your service and thereby customer retention.

• Invest in a scalable data platform
• Upgrade server and RAM
• Anticipate problems and take action

5. Ensuring efficient data integration

Data integration is extremely important to present a unified and single view of data. It is the process where data about each customer from diverse sources are combined to present a piece of meaningful information. Data integration can pose many challenges for a data aggregator. It includes issues like you don’t find the data in place or real-time data when needed, data is not formatted accurately, data is poor quality, duplicates in data, and so on.

• Invest in a smart data integration platform
• Use automated data integration tool
• Use data transformation tools
• Data stewardship for data health

6. Having streamlined pipeline for capturing each data type

The data pipeline is used to integrate data from different sources into a common destination enabling data analysis leading to business insights. The pipeline also ensures data consistency which is important for accurate business insights. Data could be structured like phone numbers, location, etc which need fixed format for retrieving and saving while the unstructured data like social media comments, online reviews, etc. don’t work well in a fixed format.

You would need specialized systems for data pipelines for the efficient migration of data. Building a data pipeline has its challenges like every time you add a new data source it will need to be integrated into the pipeline. A faster data pipeline is needed to help your business intelligence with real-time data. Your data pipeline should be able to handle any change in data without breaking it down.

• Manage your data pipeline carefully
• Automate the transformation of data
• Outsource your data pipeline project to experts

Outsourcing data acquisition

Outsourcing data acquisition to experts in the field can be more cost-effective and fruitful. There are already experts in the field with the right resources and infrastructure equipped to handle data acquisition of any volume and complexity. Trained at the acquisition of structured and unstructured data of various types; outsourcing them the data acquisition task instead of spending time, money, and efforts and focus on core and revenue-generating activities is a smart move.

Conclusion

Considering the huge flow of data from multiple sources data aggregators have a huge task to structure and present accurate B2B data to marketers for effective and successful marketing campaigns. There is no doubt that data is available in abundance but acquiring the data from diverse sources in varied formats and further structuring the data needs specialized skill set, technology, and infrastructure. Just acquiring the data will not serve the purpose unless the data potential is unlocked.

As a B2B data aggregator, to be able to draw actionable insights out of the data acquired for your clients, you will need to have a deep understanding of the client requirement, have a strategy in place, keep technology and infrastructure updated all the time, have a global mindset and knowledge of global privacy regulations, keep the system scalable, have an efficient data integration system in place and finally be open to any adaptation in the system due to changing business requirement and evolving technology.