Until organizations get an operational context that can respond to new capabilities, the project can be technically successful, but never deliver enough business value to gain support.
1. Have a Business Goal in Mind
It needs to be clear what the outcomes of the project will
be, who can/will benefit and how they can/will benefit. It is critical that at
the end of the day you have the right team in place to use the insights that
come from the projects, what better decision will you be able to make as a
result of the analysis and how will you drive and track those better decisions,
It is understandable that many BDA projects are started but
in a competitive internal environment, so most of these projects have no
defined business driver or requirement. As long as Big Data projects continue to be treated as pure IT projects, the
failures will continue to pile up. Getting the machinery running is generally
not the issue, it is the failure to deliver value because the end-users and
core decision makers are ill equipped to "get it" and can not extract
and use the inherent value in the mass of data at their fingertips. Converting big
data, into understandable, digestible, useful and actionable information is the
business purpose from Big Data project.
2 Assume Data Imperfection & Immature Technology
Data will be incomplete and less than pristine (duplicates,
incompleteness etc.) in the Big Data analytics, and the analytics must
accommodate the real state of the data. By the time a project gets going,
there's probably around 30% more data (both in volume and diversity) available.
The analytics will need to be refined and adapted because you don't know
everything when you start. Your framework for developing and deploying the
analytics should support an iterative development approach
Lack of resources,
high data volume, new technology
that is not production ready, variety of data sources and high data volume is
plaguing the industry. This is also encouraging innovation at a very fast rate,
but it will take time for industry to mature and stabilize and then the success
rate will increase and will come more in line with industry standard success
rate of projects or even higher.
3. Big Data Talent
The pitfalls of most of these projects is because Big Data
seems like a disruptive technology and the old users of the systems are not
equipped with these new concepts and will go a long way to see it fails.
There are generally two distinct
skill-sets, technical on the architecture side and the analytical skill set to
create action based on the information. You need a solid team for both to have
a shot. If not you wasted time, money, effort and potentially damaged prospects for the next projects coming down the pike. For example if your new
project can tie your CRM
"The statistician is no longer an alchemist expected to
produce gold from any worthless material offered him. He is more like a chemist
capable of assaying exactly how much of value it contains, and capable also of
extracting this amount, and no more. In these circumstances, it would be
foolish to commend a statistician because his results are precise or to reprove
because they are not. If he is competent in his craft, the value of the result
follows solely from the value of the material given him. It contains so much
information and no more. His job is only to produce what it contains." Ronald
A. Fisher
4. Collaborate Effective Processes with Right Talent
Poor planning and coordination are organization and project
management related, however, the nature of most big data projects requires the
ability to read and rapidly process variety of data, high volume data sources,
making coordination critical. Things get complicated very quickly as the
structure of these data sources and the business needs continue to evolve. If
organizations are unable to staff the right kind of data scientist(s) who have all
the skills to take the project forward, and understand the business needs, they
end up staffing a lot of individuals to fulfill the skill set needs. This
creates a communication and coordination issues...communication touch points
increase with the square of number of individuals involved in the project ..
and we know how things go from there.
That has remained constant, or at least linearly increased,
is the number of prospective users of data who seek magic over math, confuse
computational speed with statistical precision, and generally would prefer the
answers they seek over the answers that past analysis has given them regardless
of scale.
On the business side, Businesses haven’t yet fully
understand how to redesign business processes so that they can take advantage
of what big data can potentially tell. Poor cooperation, poor planning and lack
of skills should be categorized under organizational failures rather than Big
Data centered project failures though.
5. The Big Data Life Cycle Management
The life cycle of big data is to evaluate capacity planning
for Big data services through timely intervals and expected growth cycles.
The velocity and a metric for relevant time sensitivity is important in the
collection processes of what you are analyzing and for what purpose pursuant to
the predictive analytics needed to support the model coupled with levels of
complexity.
For a couple of
decades, IT has been pushing towards a "single source of truth" in
data management. A lot of money and technology is aimed at getting everything
to line up and agree. There is a whole "Master Data Management"
industry evolved out of the desire for consistency and correctness - never mind
the legal requirements to get the data "right". Big data often
threatens all this and makes a lot of IT shops nervous, so projects get
sidetracked, slowed or abandoned because it takes the new angle and multi-dimensional lens in capturing insight upon Big Data.
Until organizations get an operational context that can
respond to new capabilities, the project can be technically successful, but
never deliver enough business value to gain support. In that situation you
probably won't get to do another one.... Big Data has all tough
characteristics, how to build the Big Data modeling framework as a
systematic approach to master volume, velocity and complexity needs to be
thought through.
Mastering Big Data is more as journey, not a one time
project,. It is the nature of the beast that "noise over signal"
dominates every epistemological effort. A certain amount of project failure
ought to be tolerated if innovation is being sought. With considering all key
factors listed above, organizations can make progress in driving Big Data
success.
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Tag: PM201A52. Let me share all of you about #5 Tips for Project Management Success,, I hope you enjoy it
1. Plan your day using time management techniques
As a project manager, time management skills are essential because you are dealing with a wide range of tasks that demand a quick turnaround time. Planning your day will go a long way in keeping you organized and increasing your productivity. Assist your task planning by using project management software which helps you track the work of you and your team.
If you are not very tech savvy, a simple to-do list can also be a great organizational tool. Prioritize your most important tasks by putting them at the top of the list and less important ones at the bottom. Having a visual plan of your daily tasks helps to keep you on track and aware of time.
Related post: Free ebook 104 secrets to become a great project manager
2. Include stakeholders in important project conversations
While you will have plenty of responsibilities regarding the project, don’t neglect your clients.
Good communication is essential is keeping both parties informed of project progression, curtailing scope creep, and apprised of changing requirements. Some clients may have different expectations when it comes to communication, so make sure to establish the frequency and type of communication (like emails, phone calls, and face-to-face conversations) at the beginning of your project.
Establishing communication expectations early helps alleviate stakeholder uncertainty about communication frequency and delivery.
3. Regularly communicate with your team
Daily team communication helps keep misunderstandings and unclear requirements under control. Keeping your team informed in every step of the project is essential to project management success.
For example, a study published by Procedia Technology found that good communication skills were the cornerstone of project management. The study examined over 300 “construction project managers, architects, construction managers, engineers and quantity surveyors” and their successes and failures on various construction projects.
4. Anticipate project setbacks
Even the best-laid plans often go awry.
Remember that even with a high amount of planning and attention to detail, your project may still encounter some challenges. Pay attention to complaints from stakeholders or colleagues, and other warning signs, like a missed deadline or cost overrun, that there may be a problem.
Preventing a crisis will keep your project running smoothly, save you a lot of time, and keep you, your team, and your stakeholders confident in progressing with the project.
Unfortunately not every complication can be avoided. Crisis management skills are essential for dealing with the unexpected. Project managers need to be flexible and pragmatic. Improvise and make sharp decisions when needed.
Related post: 92 free project management templates
5. Stay focused on the details
A common problem project managers encounter is having the project aims not aligned with the organization’s objectives. A great project manager will strategize a plan for the project to lead back to the overall success of the business.
Know your project’s scope by heart and avoid wandering outside of the project’s requirements. It’s too easy to get lost in minor details and forget what your focus is, so a well-planned project scope is essential for success.
And final, you should use KPI to measure effectiveness of the project, here are full list: 76 project management KPIs
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