DemandMatrix Blog

B2B data | 6 min read
Written by Paroma Sen
on February 01, 2019

Big data can add immense value to your overall business by providing new insights that enable more focused and better decisions. In fact, Big Data is becoming more and more vital in order to provide a better customer experience and beat the stiff competition in the B2B marketplace.

Why the Big Demand for Big Data today?

The B2B marketplace is riddled with change and competition. With the right kind of data set and insights, companies can more easily adapt to the dynamic environment and focus on narrower segmentation to provide more targeted products, solutions and services to their customers.

The right kind of data, data provider, insights and intelligence are essential to properly analyze customer needs, and to even predict them.

Nischal Vora, currently the CTO at DemandMatrix says, "Data is the New Oil and every enterprise, irrespective of size, is sitting on huge reserves. Big data paves the way to tap it, to unleash and realize its potential. Companies with capable big data practices or implementation will be the only ones that are future-ready, and they'll be the only ones to understand customers better while being well-prepared to face competition."


Data Vendor blog

So the key question here is, how do you choose big data solution providers to help get that competitive edge?

Choosing data vendor blog-1

Here are a couple of things to consider before buying into a big data solutions provider.

1. Have clearly defined company objectives

Once teams involved in any particular function or process identify the core company goals they want to achieve with set timelines, it is easier to drive better decisions and purchase a big data solution and data set that will be a best fit for the organization’s needs. Without identifying core objectives first, picking a big data solutions provider will be akin to flying blind. In a data-driven marketplace, this is the basis that will impact the effect of your strategies and plans.

2. Attention to Scalability

According to an earlier IDC study, the volume of data stored in all digital systems is increasing by 40% every year.

Here’s something to draw attention to when you read this statistic--> when companies scale, they will experience a need to store more data at a much faster pace.

In an earlier article we came across, Jeff Healey, Director of Product Marketing for Vertica at Hewlett Packard Enterprise had said “it's important to choose a technology that can scale as you grow and position you for success – as your business changes and data insight moves beyond just experimental."

The key takeaway here:

  • Always choose a big data solution that will have room for scalability but not at extreme extra costs
  • Your big data solutions should compliment market dynamics and changes as data volumes increase
  • Sudden increase in big data volumes can create performance issues. So it’s important for your data solution to scale and deliver consistent performance, all at the same time.

3. Ability to handle different types of data

To quote Sangram Vajre of FlipMyFunnel fame, “most people don't have the right data”. 

What becomes challenging for companies to handle are the various kinds of data or insights they gather, over time.

So what should you be looking for?

Typically, a big data solution should be such that it can handle a variety of data. It should support both structured and unstructured data, in multiple formats, along with the ability to support big data tools.

4. Leverage your current platforms/tech stack

The kind of insights and types of data that any companies use are subject to change with time. To keep abreast, companies have to invest in new platforms, insights or solutions regularly, especially today.

But does that mean that they should get rid of their existing storage, data management and analytics systems?

What works better is evaluating current solutions to measure how exactly they are falling short when it comes to their big data needs.

Once you have this in place, leveraging the best features from your current stack while also closing in on gaps by investing in a new platform will make sense.

This will also help identify which solution you should retain or renew and which to ditch!

An important point to remember when assessing your current tech stack is ease of integration. It is important to assess how easy or difficult your proposed new big data solution provider will be to integrate with existing big data solutions/platforms and data types.

5. Ease of Use

Another important factor here is the burden a new technology or solution will place on existing staff. When it comes to technology, up skilling is crucial because new technologies evolve. If a new big data solution is going to force analysts and Business Intelligence professionals to relearn new tools or limit use of existing tools – it’s up to you to measure if this dynamic is worth it in the long term.

Key takeaways here:

  • Be sure to choose a vendor that works with popular tooling for ETL, overall data management, data visualization, data analytics, Business Intelligence, etc. on-premise and in the public cloud.
  • Leveraging the resources you already have can also help to keep expenses low.

That automatically brings us to the next factor, cost!

6. Overall Cost and Budgeting factors

Budgets are important for every company. For any technological investment or big data initiative, it is important to consider the cost of storing, managing, maintaining, analyzing the systems and data.

When adding a new Big Data Solution or migrating platforms, it helps to calculate the total cost of the project.

This typically includes cost of learning or upgrading skill sets too (and time involved), administrative and professional costs.

Some key points worth considering at this junction:
-Evaluate the overall cost implications of each option
-Evaluate the total value that will be delivered in the long term

7. Open Source Standards work best

Most popular Big Data solutions like Apache Hadoop and related projects are available under open source licenses. That’s why several organizations often look for options that can be supported by these projects.

The advantage of opting for a solution that is based on open standards:

-Freedom to migrate to new vendors

Most experts advice organizations to look for solutions that are based on open SQL standards with in-built database analytical functions for IoT, machine learning, pattern recognition, pattern matching and more.

Also, it is important to note that the ability to integrate with open source and complementary technology helps avoid vendor lock in and provides more flexibility.

8. Security is a Priority

With the complexity of technology and networks growing everyday, the sophistication of cyber security threats has increased.


  • There were one billion malware-based incidences between June and November of 2016
  • The estimated cost of cyber crimes is $ 1Bn
  • 99 percent of computers are vulnerable to cyber attacks at any given time

Source: Datameer 

Big data solutions are now more mature and offer improved security features. However, most solutions don’t cater to end to end security.

As an organization, whatever solution you choose to go for, ensuring security of data shouldn’t be dependent on only what the platform provides.

9. Easy Accessibility

Any solution or platform that a company uses should be easy accessible to global teams. What can boost or extract most value from a solution is when tools don’t need users to be experts in order to use them to draw the most relevant insights from it.

A good data driven culture and system within an organization would mean making systems as DIY as possible. Discovery of data and insights and making data analytics ubiquitous to all users should be the core goal of any new big data solution adoption debate.

Furthermore, if every team is able to churn out value and meaning from those insights easily, it would create greater impact.

10. Rely on Testimonials and References

While it is common to ask new vendors for valid use cases to understand how their solution has helped customers in the past, reaching out to get your own testimonials and references might work better.

That’s because you’ll have first-hand feedback from the horses’ mouth itself.

What you can do is:

  • Ask vendors for recent reviews or references and testimonials as a starting point
  • See how customers have implemented the new solutions and the effect it has had by referring to use cases
  • Ask your vendor to get you in touch with a client or two so you can get some first-hand information for yourself


When you do that, it won’t hurt to seek real-time advice and tips on their experience using a particular solution you are interested in. This will help your vendor selection and solution deployment process run smoothly.

At the end of the day, what’s most important is understanding what your company needs before investing in any particular big data or business intelligence solution.


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