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Best Practices for Deploying Your Big Data Analytics

By on October 18, 2016
Best Practices for Deploying Big Data Analytics

 

So it’s time to choose your big data analytics solution. It’s not exactly a stress-free process. After all, in the seventies IBM came out with the brilliant slogan, “Nobody ever got fired for buying IBM” because they understood exactly how risky it can be to spend big budget on IT solutions.

Four Best Practices to Deploying Big Data Analytics

There’s a lot that goes into choosing the perfect big data analytics platform. There are so many vendors claiming to have what you need, so how do you wade through the complicated recipes and solution guides?

I’ve put together a best practices list, a blueprint to guide you through choosing and deploying the big data analytics software that’s right for you.

1. Understand Your Organization’s Big Data Needs

While the tendency is to go out there and start looking up different software vendors right away, I recommend that as a future step. Listening to vendors tell you what you need isn’t the first step to choosing the right data analytics software for you.

No, the first step is working with your key stakeholders to understand the special needs of your organization.

Too often, people will declare, “We need more data, better data, cleaner data, we need a Hadoop cluster, we need more business intelligence, we need a dashboard, we need visualization!”

All this translates into the need for a very simple process in the minds of the business: “I need data that I can analyze, that I can share,” without getting mired in the details of how this gets done. This is a core essential of what most organization need and want.

Here are some questions you can start by asking to draw out what your organization needs:

  • What products or solutions are you using today to deliver analysis to your stakeholders? How would you rate the solution? (Love, like, hate — easy to use, moderately easy to use, or hard to use)
  • What do you use these products for today?
  • What analysis, reports or data would you like to have that you don’t have today?
  • How would you improve your system or processes if you could?

I think of this step as the fun part. It’s exciting to hear what gets people enthusiastic about analytics. And I love understanding the vision people have about how it can help solve their problems and open doors to business possibilities.

2. Organize Your Big Data Priorities

Now that you’ve started people thinking about what big data analytics software can do, it’s time to really nail down the priorities.

I always recommend a use case assessment or workshop for this step. At Datameer, we like to hold a half-day workshop that helps attendees:

  • Identify key business use cases
  • Align teams on priorities
  • Secure that really essential business buy-in on projects

As part of the workshop, we have stakeholders from all parts of the business brainstorm problems or needs, assign values to each one and then prioritize them. It’s an interactive way to see all of the different big data needs your company has, and then figure out a way going forward to tackle them.

Gather this information together and create a grid that ranges from high-need essentials and low-need nice-to-haves. Keep this on hand as it will help you determine what solutions you will need to purchase and what existing systems you can build or modify — once you have your big data analytics platform picked out, it will help determine your first projects.

3. Analyze Your Big Data Analytics Needs

Once you have those business needs and use cases, it’s time to prioritize. Take a look at the use cases you have now, and match these with solutions. What can you currently handle? What do you need with a new solution?

Here are the questions I recommend asking:

Infrastructure:

  1. What do you have installed today?
  2. What can you reuse?
  3. What can you use better?
  4. What do you need to buy?

Solutions:

  1. Which solutions that you want to purchase meet the needs of the high-priority use cases?
  2. Which solutions can you purchase that will have longevity? Recall that a part of this process is the rinse-and-repeat characteristic of analysis. Analysis doesn’t happen as a one-and-done event; rather it’s a journey and the path winds. The blueprint for success can be executed many times before building and deploying the correct structure. Select a packaged solution that delivers all the capabilities you need today and some capabilities that you want for tomorrow.

People

  1. Who are the people experts who have interest in building new solutions for your business?
  2. What can you easily do today?
  3. What are these business users’ career aspirations, and does this solution fall into that?

The people part of this is often forgotten. But it’s the key to success. For your new purchase to be successful, you want as many people as possible to be not just supportive, but excited for this new platform.

Tie your new purchase with as many long-term projects as you can, whether it’s uncovering new business opportunities in the company, or growing people’s careers. They’ll be invested in this new purchase and work with you to push it forward.

After you put work into performing an analytical inventory of the three key areas outlines above, you’ll be ready to present your strategy and long-term vision for your big data analytics journey.

4. Get Your Stakeholders Excited About Big Data Analytics

I lay this out in three steps:

  1. Socialize
  2. Improve
  3. Organize

Socializing Your Big Data Analytics Project

When I tell people this, I often get confused looks. “Socialize?” they ask me. But most businesses realize that the socialization of the plan needs to happen. People have to get excited and looking forward to new tools and ways to deliver the content to their stakeholders. Do it in the right way, and they’ll become an organizational change maker or hero.

In addition, you want buy-in from as many people as possible and that means making them feel like they’re a part of the process and the project. Talk about your ideas before you get too far down the road. You want plenty of feedback so you know which objections to handle later on, or solutions to brainstorm so you’re not caught off-guard by unaddressed questions during presentations.

Improving Your Big Data Analytics Project

You want your stakeholders to feel invested in the solutions that are being rolled out to them. By incorporating their insights into what needs to be done, these people will be more willing to:

  • Attend training
  • Spend time delivering constructive feedback on the experience
  • Share with others the desire to use the tools

This allows the platform to enjoy a level of grassroots growth that can’t be implemented or instilled — it must come from the entire organization. Your big data analytics project has the greatest chance for success if it organically evolves from within the business.

Constantly reevaluate and ask whether you’re optimizing and evolving the solution as needed. Remember, the big data analytics environment is constantly changing and growing bigger so you’ll want to be sure your choice will be continue to be a good choice down the road.

Organizing Your Big Data Analytics Process

Last of all, make sure your process is organized. You want your stakeholders and solution providers to feel on top of things, and ready to charge in there to support your decision. Ensuring transparency between everyone will drive agility, rapid implementation and high adoption.

Your Path to Big Data Analytics Success

I’ve laid out my best practices to choosing the right data analytics platform, and also guaranteeing its success. This process will help you gain a stronger understanding of your business’ data evolution and its needs for an analytics solution. I wish you the best of luck!

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Joanna Schloss

Joanna is Datameer's Director of Product Marketing and is highly experienced in data and information management. A contributor to CMSWire, her areas of expertise include big data analytics, business intelligence, business analytics and data warehousing.

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