PM Knowledge Areas

Integration Management

For big data projects, an additional integration consideration should be noted. Can the data found in the big data environment be integrated with existing analytical data?

Also consider if business applications need to integrate with existing analytical tools, RDBMSs, or other software.


HR Management

Have you done a gap analysis of skills?

Will resources with right blend of talent and skill sets be available?

Do you have the expertise to “connect the dots”?

Do not create a team consisting primarily of IT experts. Success requires a blend of talent. According to Rich Katz, CTO of Elemica,

“creating a cross-functional team including operations, engineers, technology, sales, logistics, finance, etc. is instrumental in tying analytical efforts back to specific business objectives.
By creating a collaborative environment of intelligent, cross-enterprise decision-making, and a pervasive data-driven culture, executives are able to make more informed decisions that drive positive change in their businesses,”
he says (Building Off Best Practices).

Will vendor support be required? Has an evaluation of vendor capabilities and skill sets been conducted to ensure that they have required qualifications and expertise?

Do you have a training plan? What training will be required to ensure readiness of resources to contribute to the success of the effort? What training will be provided?

Does the team know how to perform sophisticated analysis on big data?

Is there a fundamental understanding of the concept of textual disambiguation? Big data is unstructured, thus context in the typical sense is lacking. Since context is necessary for analytical processing, the team must understand how to do textual disambiguation.


Scope Management

Requirements

Big data projects involve uncovering insights through experimentation and evidence-based findings, thus an iterative methodology lends itself well to such projects. Thus, let’s review such concepts as iterative, rolling wave planning and progressive elaboration.

Iterative Terms

For big data projects, be sure to consider scalability and performance needs in addition to your functional requirements.


Tasks

Because big data projects can get pretty complex, experts recommend breaking the work into general categories and then drilling down into each to create a solid plan.

Some common technical tasks include:


See also Requirements Management for Big Data Projects.


Time Management

Big data projects involve uncovering insights through experimentation and evidence-based findings, thus an iterative methodology lends itself well to such projects. Thus, concepts such as rolling wave planning and progressive elaboration will apply.

Iterative Terms

Some time saving tips from Cynthia M. Saracco, senior solutions architect at IBM’s Silicon Valley Laboratory.


Some additional time saving tips from EnterpriseProject.com that may help save time, decrease chance for error, and reduce risk:


Cost Management

Big data projects involve uncovering insights through experimentation and evidence-based findings, thus an iterative methodology lends itself well to such projects. Thus, concepts such as rolling wave planning and progressive elaboration will apply.

Iterative Terms


Risk Management

An iterative approach that breaks down the project into smaller chunks will reduce risk to your big data project. Smaller, more focused implementations are easier to manage and make it easier to respond to issues, changes in needs and requirements, and even changes to data.

Computer Weekly provides tips from ISACA to address risk and improve the organisation’s ability to use big data to meet its business objectives:

How to Manage Big Data and Reap the Benefits


With big data projects, the vast data provides knowledge as well as exposing the organization to increased risk. Organizations are vulnerable to the risk that the data could get into the wrong hands.

BaselineMag.com provides information to improve big data governance and offers five questions that should be asked that can help you secure your data. The article recommends as a starting point asking the following questions:

“When it comes to a company’s plan to improve big data governance, that process should begin by asking the right questions, such as these five:"

  1. Can we trust our sources of big data?
  2. What type of information are we collecting, and are we exposing the enterprise to legal and regulatory challenges?
  3. How do we protect our sources, our processes, and our decisions from theft and corruption? How can we improve on this?
  4. What policies and processes do we have in place to ensure that employees keep stakeholder information confidential during and after employment?
  5. Which of our actions might create trends that can be exploited by our rivals?

Procurement Management

Vendor management is troublesome for many IT projects. big data projects are no exception. Vendors may not have required qualifications and expertise.

“There are hustlers and charlatans who promise the world but don’t produce results. These vendors realize there’s a lot of hype around big data and behave accordingly. Many legacy consultants and systems integrators have positioned themselves as experts despite their lack of qualifications (The Big Data Wild West: The Good, The Bad, and the Ugly).”


Resource Selection

According to Tom Deutsch, Program Director on IBM’s Big Data team, select the people before the technology.

“This may seem a little counter intuitive, but we've learned that selecting the people who are sponsoring and staffing the project is actually a more important predictor of success than the technology. When dealing in emerging technologies, personnel selection is the biggest variable in the project’s success. It is not good practice to put people in the critical path of your project if they are exceedingly vested in existing approaches and technology or if they are hostile to change and not open to new technologies."

Technology Procurement

Technology leaders must buy into the overall feasibility of the project and should provide design and implementation inputs. At that point, you can evaluate technical options which might include Hadoop, a relational DBMS, a stream processing engine, analytic tools, visualization tools, and other types of software. You will need to decide if the solution should be a cloud, on premise, or hybrid solution.

According to Cynthia M. Saracco, senior solutions architect at IBM’s Silicon Valley Laboratory a combination of several types of software is often needed for a single big data project. Every technology has strengths and weaknesses, thus Cynthia recommends that you understand enough about the technologies you will use before moving forward.

If you decide to use Hadoop on your project, she recommends giving serious consideration to using a distribution that packages commonly needed components into a single bundle. This will minimize the time required to install and configure the environment. You will also need to consider your business applications. If business applications need to integrate with existing analytical tools, DBMSs, or other software, you should look for products that have some built-in support for that.


See Also:

The Impact of Big Data on Cloud Infrastructure Procurement


About Patti Gilchrist

Patti Gilchrist

Patti Gilchrist is a Sr. Technical Manager with 25 years experience implementing strategic enterprise initiatives.

Patti has a reputation for effectively translating business problems into innovative solutions and creating strategic roadmaps to achieve business goals.


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See Also

How to Successfully Implement a Big Data Project in 8 Steps

8 Proven Steps to Starting a Big Data Analytics Project

The Big Data Playbook

How to do a Big Data Project: a Template for Success


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