Too often, managers invest anywhere from tens of thousands to tens (or even hundreds) of millions of dollars on the latest business buzzword. Many are ill-suited to the firm that adapts them, others were ill-conceived from inception; still, others are eclipsed by the next big thing. Investing in Big Data is an expensive proposition from both a hardware/software and a human capital perspective. And with many a manager having been burned by a bad investment in a management trend, you’ll pardon their skepticism when you breathlessly blurt out the benefits of Big Data, along with a purchase order for approval.

Big Data is – well, big. It encompasses not only all digitizable data of reference to firms, but also the tools, techniques and technologies that are used to manage said data. This, for many managers, is abstract at best and overwhelming at worst.

You may believe that Big Data is right and necessary for your know that this is right for your firm. But your manager doesn’t – not yet. Perhaps he or she has evinced some curiosity about the subject and has given you an opening to convince him or her that this is more than filler for a business periodical.

Or perhaps you’ve encountered a marketing problem you can’t quite address without certain data your firm does not collect currently. And solving this problem would be tremendously lucrative for your firm’s overall revenue – not to mention your career.

Or perhaps you’ve learned that your closest competitor is spending in this area right now. And now, you fear that in three or five years down the road, while your managers are spending on Big Data under financial duress, you’ll need to be shopping your resume.

Whatever the necessity for convincing your boss of the value of Big Data for your firm at this time, there are certain approaches that are more effective than others.

How to Convince Your Boss of the Value of Big Data for Your Company

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In this article, we will cover 1) how to approach your supervisor as non-C-manager about the value of Big Data; 2) key focal points of your pitch; 3) questions you should be prepared to answer; 4) how C-suite managers can approach the CEO about the value of Big Data; and 5) how IT personnel can start the conversation about Big Data.


Big Data is not just big; it is growing bigger every day – both the data itself, which grows with every sensor log and keystroke, and the tools to manage it, which grow with every new firm that embraces Big Data.

Because Big Data is so broad and abstract, it is critical that you both tie the adoption of Big data to the firm’s existing operations, and that you present potential technologies in as concrete and specific a fashion as possible. It is also wise to start small – with your department, rather than to discuss pitching a firm-wide adoption immediately. Small deployments allow you to test technologies, work out pain points, develop processes and procedures, get buy-in from other staff, and minimize risk.

Begin by reviewing your department’s business goals – especially those that generate revenue. This should include your manager’s specific tactical goals and responsibilities. Then identify those challenges encountered by your manager and your department that are grounded in incomplete information. For example, if your department is responsible for reducing overall production times in a plant plagued with frequent equipment malfunctions, you might need a system for forecasting failures, or better yet, for automatically scheduling preventative maintenance. Identify several goals and several challenges, as well as the insights needed to address them successfully.

Next, identify several aspirational goals aligned with the firm’s vision and strategic plan. For a hypothetical retailer, whose vision is to be the market leader in the primary regional market they serve, such goals might include providing the best customer service, carrying the most extensive product mix, and/or having the fastest delivery service.

As you begin to prepare your presentation, prepare to discuss how data science will address the specific goals and challenges you currently face. Then explain how data science will prove a source of competitive advantage for the firm. When discussing current challenges, start to research specific management technologies, but only cursorily, unless you have already spoken with, and have buy-in from influential IT (Information Technology) staff. This is crucial. If you are outside of IT and have limited technical experience, you will likely need to discuss with someone in IT what solutions are available on the market, and how they are compatible with your current system. It helps if you have this conversation after you have explained how a Big Data management system will 1) make their jobs easier; 2) not create undue additional work; 3) will not cost them jobs; 4) will be otherwise politically feasible.


Your pitch or presentation should depict the need for an internal data science apparatus within this context. Connect data science to solutions. Discuss this by directly requesting the resources needed to address the core challenges, and then discuss how Big Data can transform business as usual. Depending on your department, you may focus on one or more of these factors more than others, but you should be prepared to discuss:

Big Data as a source of competitive advantage

Ideally, the insights from Big Data should drive firm-wide strategy. However, if you are not currently in the C-suite or lack influence over those in it, then frame the argument in terms of how your department’s use of Big Data could be a source of competitive advantage for the firm, and/or how its use could increase revenue.

Big Data as a source of innovation

Big Data can transform an R&D department into an innovation powerhouse. Paring data mining and statistical modeling tools with traditional market research techniques can yield powerful and actionable insights for the development of new products.

Using Big Data to increasing marketing effectiveness

Data scientists can create mathematical models representing their target consumer to forecast their behavior. This can inform to whom firm’s should promote its products, where those products should be distributed, how they should be packaged, and other key marketing decisions.

Optimizing decision-making using Big Data

A major selling point of Big Data is one’s ability to use it to inform strategic decision-making. Predictive analytics – Big Data’s tools and techniques for forecasting, allow decision-makers to develop mathematical models that they can use to predict the probability of the success of a decision and its alternatives.

Building stronger teams through people analytics

Many firms that have invested heavily in, and appropriately implemented, people analytics – the application of Big Data’s tools (particularly predictive analytics) to human resources, have seen decreased turnover, recruitment, and onboarding costs, and increased productivity, innovation, and revenue.


With some input from a member of IT, you should also begin to describe what your initial foray in Big Data should look like. Use comparables as a starting point when estimating the resources you need, but ultimately, your pitch should be based on the specific needs, working environment and budgetary constraints of your firm. The following is a list of questions, most of which (if not all), you should have answered before you make the formal pitch to your boss(es):

  • What new data, data streams and data types will your firm now process?
  • What are the new storage and processing needs, from an IT perspective?
  • What changes are there to the roles and responsibilities of IT, and other personnel?
  • How will your firm implement this Big Data solution technically?
    • Do you have an existing system you can use to process data at scale?
    • Will you need to deploy a Hadoop-based data processing system in-house?
    • Should you look to a cloud-based vendor, such as Amazon Web Services for your Big Data needs?
    • Will you look outside to a vendor like Mu Sigma, for your both your Big Data management and analytical needs?
  • How does the proposed technical solution fit into the existing IT environment?
    • Are the proposed systems compatible with existing systems?
    • If you need new systems, how will you phase out the old ones and deploy the new ones?
    • What is the estimated cost of phasing out legacy systems? This should include both the physical hardware / software and the manpower associated with phasing out the legacy system(s), and training of personnel on the new systems.
  • What provisions – both technical and procedural – will be implanted for the security and privacy of the new data?
  • What new personnel (numbers, costs, and qualifications) will your firm need?
  • Will needed personnel be project-based consultants initially, or full-time staff?
  • Are their existing staff members who, with training, can be utilized in this initiative?
  • What are training needs for end-users across the department?
  • To whom will the new personnel report?
  • What processes and procedures will be developed to utilize the data once it has been processed? What does the workflow look like?
    • Fundamentally, your Big Data workflow will contain four key components: acquisition and storage, refinement, analysis and curation, and distribution. These components will look slightly different in every organization; show what they will look like in yours.
  • What are the explicit goals of this initiative?
    • You should discuss both the short-term goals that drive this pitch, as well as long-term goals involving the firm’s eventual holistic adoption of this approach.
  • How will your firm measure the success or failure of this initiative?
  • What is the project’s budget?
    • Forecast a budget that is accurate as possible, but discuss the possibility for cost overruns realistically.
  • What is the projected ROI (return on investment)?
    • Provide conservative estimates, with an emphasis on revenue-generation and productivity cost savings.
  • What is the impact, if any, of this deployment on short-term departmental goals?
  • What is the competition doing regarding Big Data?

Your presentation should incorporate these questions, and summarize your answers to them without overwhelming. Play to your boss’ preferences, as a Big Data marketer would. What is important to him professionally? What motivates him? What kinds of presentations has he responded to in the past? Tailor your pitch to your boss, in terms of style, length, and format, using all available data.


If you are in the C-suite, you have the opportunity to push for a firm-wide adoption of Big Data. When you do should depend on the firm’s capacity and financial constraints and needs. In addition to the above questions, consider:

  • How does Big Data inform your firm’s short-term goals and long-term strategy?
  • What benefits of Big Data add value to your firm’s brand and strategic vision?
    • For example, if your firm promises to provide excellent customer service, then examine how Big Data can enhance customer service at every brand touchpoint.
  • What are the benefits of implementing Big Data at this time?
    • Examine your market performance and how new insights can drive revenue and profit margins.
  • What are the risks of not implementing Big Data at this time?
    • Envision scenarios where you lose market share to the competition using current strategies.
    • Project the effect on your market(s)/industry if you increase market share through a Big Data-driven new product offering, service, or sales strategy, as a hedge against the competition.
  • Does your firm have the internal capacity to manage Big Data?
  • Does your firm have the financial resources for a Big Data deployment?
  • How can data management become a long-term core competency?

Ideally, you should launch a Big Data initiative as a pilot project in a single or in several departments, and then implement a rolling deployment to identify and remediate any issues. This is not only best practices, it allows you to pitch an abstract concept to the CEO as an initiative that should be tested, and that can, if successful be a vehicle of transformative change. 


If you are pushing for the adoption of Big Data management tools and technologies and you are in IT, you may have seen an increase in the variety of ad hoc requests for information from other personnel in the firm, some perhaps even necessitating external vendors to develop customized solutions to derive said information at cost. Or you may have had to address an increase in the amount of maintenance performed on legacy systems as the volume and velocity of your data has increased with time.

In these cases, productivity can be a very compelling argument for persuading your manager to implement a Big Data hardware/software solution. Make an informal audit of your firm’s various databases, and the information that is commonly requested from those databases. How many of those are automatic or require some manual manipulation before those reports can be distributed? Take a look at the quality of those reports, and how consistent the data is in those databases. Deploying Big Data technical solutions as a way to generate timely and accurate reports, reducing ad hoc requests, and automating data cleansing, may be enough to get your supervisor, and hopefully even your CIO onboard.

Perhaps you’re a forward-thinking individual, and realize that while there are no problems with existing solutions now, as the volume, velocity, and variety of data increase over time, there will be a problem in the future. Moreover, you realize that there are opportunities available in the midst of all that data. In this case, it is helpful to identify influential high-performing actors in your firm who have either driven change or are responsible for critical functions. Examine their pain points, and how a Big Data solution might address those pain points. If it is feasible politically, approach that individual, and discuss their issue more in-depth, sharing the outlines of your proposed solution. If not, examine available data and design a proposal that not only addresses short- and long-term data issues in IT, but also one that can be adopted by at least one other department to drive revenue.

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