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Harness These Six Big Data Skills and Never Lose a Deal Again

July 07, 2017

Harness These Six Big Data Skills and Never Lose a Deal Again

Big data is rapidly becoming big business, which means it also is becoming more competitive than ever. As organizations become more experienced with big data projects, they’re doing a better job of separating the experts from the wannabes. That means solution providers need to be on their game if they want to sell big data expertise and build their reputation to attract new big data customers.

Spending on big data continues to climb and is expected to hit $48.6 billion by 2019, according to IDC. However, losses from big data projects still occur, usually because the company started with a big data project that was too ambitious or underestimated the amount of manpower and resources required. As a big data solution provider, your goal is to help customers set the scope for big data projects so that the rewards and insights are worth the investment.

In order to create true value and show your prospects that they can win with big data, you need to apply certain skills in order to ensure success. Note that these skills aren’t just useful for selling big data, but can apply to any type of project.

1. Business acumen

Don’t approach big data as a solution in search of a problem. When working with prospects and customers, it’s important to take time to understand their business goals and their issues. Also, be sure to understand the various stakeholders’ needs. For example, IT professionals are concerned with data security, preserving the integrity of their network and mitigating risk. Some big data requirements may run counter to those IT concerns, but are necessary in order to achieve the results the C-level executives want. Big data can be a powerful tool, but only if you take the time to understand how to use it for best results.

2. How to apply big data use cases

Depending on the company’s objectives, you can apply big data in any number of ways to provide actionable insight:

a. Gaining a comprehensive picture of the customer. Because big data combines both structured and unstructured data for analysis, it’s ideal for understanding customer behavior and sentiment using buying patterns, [the AP stylebook has been updated for 2016 with “web” and “internet” lowercased--just fyi!] web-surfing patterns, social media and other data sources.

b. Optimizing business processes. Whether it’s information flow, manufacturing or supply chain processes, big data can be invaluable in assessing workflows and identifying weaknesses.

c. Improving security. Big data is capable of abstracting sophisticated enterprise security architectures, watching for anomalies and potential attacks using real-time analytics.

d. Assessing new business opportunities. It’s also ideal for performing “what if” scenarios, using predictive analytics in order to assess potential sales with a new product or in a new market, or for a variety of other exploratory analyses.

3. Interpersonal skills

Any big data initiative requires solid people skills, starting with the sales process. In order for big data projects to succeed, you need interpersonal skills such as leadership and collaboration, as well as curiosity and creativity, so you can build a collaborative team across disciplines. Big data is clearly data-driven, so you want to be able to promote a data-driven culture and decision-making process.

4. Cross-functional team management

You also need the ability to manage experts with disparate skill sets, and to appreciate the contributions and concerns of each group involved in the project, including senior managers, business analysts, engineers, data scientists, IT operations personnel and C-level executives. Each group will have a unique set of concerns, and building a successful cross-disciplinary team can be challenging.

5. Making the results applicable

For a big data project to be valuable , the results must be tangible and applicable in a business setting. To justify the expense, your customers need findings they can use to generate revenue or cut costs. Having domain expertise in the necessary vertical markets such as finance, healthcare or retail also helps make you an insider who is able to deliver even greater insight from the findings.

6. Delivering data-driven results

Identifying the actionable outcome from big data analytics is both an art and a science. Most companies maintain silos of data disassociated from other data sources so that each group has a narrow, insular view of their operations. They don’t understand their own data, so being able to apply that data in order to extract results seems like alchemy. However, if you find the raw data and ask the right questions, you can apply the data in order to deliver insights that can help shape the organization’s future.

When selling big data expertise, having the necessary technology background, including a working knowledge of Hadoop, MapR, data warehousing and programming, is invaluable However, your technical expertise won’t necessarily help you address the right problem. You have to start with the appropriate business context.

In order to succeed in selling big data analytics, you have to understand the value of big data outcomes. Being able to work with customers in order to see the big picture makes it easier to work backward to develop the right use cases to uncover insights that will transform the business. You can always find the necessary technical expertise needed in order to develop the big data project, but understanding how to draw the roadmap requires a different set of skills.