Big data is a promising opportunity for technology companies that sell commercially and in the public sector. Data production will be 44 times greater in 2020 than it was in 2009, and the volume of business data doubles every 1.2 years.
While this is a thriving market, it’s also complicated, and companies need to be strategic when approaching customers with potential solutions to their big data challenges. Less than 30 percent of commercial companies say they’ve successfully created a data-driven organization (and about 90 percent of companies want to be data-driven organizations).
Here are 10 tips for tackling this growing big data world:
1. Don’t try to sell everything at once.
Selling data management and analytics solutions is an iterative process, and success breeds success. If you can sell one product to a company that’s just getting started with analytics and help them demonstrate success early on, you’ll be able to continue helping them continuously uncover value from their data. If analytics is working well, it will grow.
2. Find complementary partners.
Technology companies need to partner up in order to be successful in selling big data solutions, because selling specific tools is less relevant today. You need to understand what value your customers are trying to get from their data and find partners to get them end-to-end solutions that fit their needs.
3. Target the industries most interested in big data solutions.
The commercial industries with the most opportunity right now are banking/financial services, manufacturing, health care and insurance. On the government side, think fraud and waste discovery, cyber analytics, improving customer/stakeholder engagement, asset management and health care.
4. Target the fastest-growing solutions within big data.
A big growth area within big data will be cognitive and artificial intelligence (AI) solutions – the global enterprise AI market will reach $6.14 billion by 2022. These technologies organize and analyze structured and unstructured data. If you’re selling these types of solutions, focus on banking and securities, fraud investigation, automated customer service, threat intelligence/prevention, public safety and emergency response. Health care will also be a big vertical for these solutions, including pharmaceutical research and diagnosis/treatment systems.
5. Make smart data discovery a centerpiece of your strategy.
Analytics is moving away from being solely an IT function; and smart analytics tools are helping end-users, regardless of what department they’re sitting in, make their data meaningful in a business context. They can take embedded data science tools that eliminate a lot of the legwork you’d see with traditional BI platforms and allow them to make data-driven decisions faster.
6. Help your customers meet their internet of things infrastructure challenges.
By 2025, there will be over 80 billion connected IoT devices. That means there will be significant changes to the scale, speed and structure of data management and analytics. One of the biggest challenges will be companies that have legacy infrastructures that are hardware-centric and don’t allow for the flexibility that IoT requires. Steer your customers to a multi-pronged infrastructure approach that is cloud-based, agile and scales on demand. Work with infrastructure suppliers to make sure they have this approach in place.
7. Don’t forget about cloud in your big data sales strategy.
Cloud is going to be something that affects the way analytics products are consumed, particularly for customers who have a more mature data analytics strategy. Right now, about 50 percent of BI solutions are cloud-deployed. Cloud is where customers can get more agile and combine more sets of data to get augmented analytics capabilities. IoT and AI are also going to tie in very closely with cloud and be a big part of the top cloud providers’ strategies going forward.
8. Don’t be discouraged by open source.
Open source technologies can pose a threat to companies that sell competing products in data management and analytics. One strategy is to convince your customer that if they go with an open source solution, then that solution may not be as robust. Also, it shifts the responsibility of ownership to them and not you, the vendor. You can also try a more holistic approach and align any of your tools that have open source competitors with other products or services in order to provide more value to your customer.
9.Use embedded data science as a differentiator.
The competition between BI, analytics and visualization tools can be intense, which can drive down costs. Make sure the solutions you’re selling have embedded data science solutions in them to address smart data discovery (or partner up with a solution that can add that capability)—that will help serve as a differentiator. You can also partner with service providers, since they work with customers regardless of where they are with analytics adoption.
10. Overcome the “I don’t want to share my data” roadblock.
Company culture around its data can be a challenge. People may not want to share their data because they’re territorial or have security concerns. Or they just don’t think using their data can be transformative for their business. Find the pain points within those kinds of organizations, work with those end-users or departments and help them demonstrate success and grow the business from there.
All of these tips really boil down to:
- Understanding your customer and what they want to do with their data
- Thinking long-term with clients and realizing you can’t address all their needs at once
- Always thinking about how your solutions fit into the grand scheme of things
Contact Arrow today to learn how we can help you with big data and analytics solutions.
Last modified: November 26, 2019