Big Data Projects

Businesses are continually looking for the ways of improving the
inefficiencies and the processes by implementing the big data technologies and
the associated solutions. Certainly, these chances face the variety of planning
the challenges and the projects can come at the high price in relations to the
financial cost, the implementation of the nightmares, and the people issues. Frequently
these implementations usually fail miserably; they run behind the schedule,
finish over the budget, or do not meet the business expectations. To safeguard the
successful rollout, the project team need to address the most demanding issues
about the big data projects by following some of these best practices.1. Start slowThere
are different key success factors which are used to implement big data
initiatives. Amongst them is start slow which is to start with the proof of
concept or the pilot project. Choose an important area where you actually need
to improve the decision making but which will not greatly impact. Let this original
answer a business problem you’re trying to answer. The project need be
operationalized after the results have been verified valuable and also feasible
from a point of view of the business model, encounter compliance demands which
are technologically sound. Choose
the initial project prudently. Don’t force the big data solution tactic if a
problem statement doesn’t need it. Ensure you take a time in having the right
skills in a place; there is room of learning using this project but the staff requires
at least the fundamental knowledge of the big data issues. 2. Collaborate with the project objectives in the mindThe
data teams and the business units need to work together so as to meet the
business goals. The data scientists represents the analysis using the data and
the models, and they’re expected to know what business users are actually
trying to accomplish. Conversely, the business leaders need to at least the
high-level understanding of what business can achieve with the data. The effective
collaboration needs effective communications. For instance, consider the
business intellect team which built the model of predicting the customer churn.
They consider it as the fantastic project which is based on the hypothetical
cases. 3. Have all the correct dataSometimes
it is unlikely to answer the particular questions due to the data not
available. Also when a data is there,
enterprises are not always sure they are asking the correct questions. The
project needs to deliver the measurable results which have the impact which
means having right data and also leveraging which data effectively. You may run
very refined regression and then build very compound models which can be
exciting but the bottom line there in is delivering to business assessable results.
To be effective, you need to decide the questions you can actually answer and
then determine if these questions can be answered using the available data. 4. Do not skew the resultsThe
human tendencies and the non-representative data sets incline to skew the results
and result to the incorrect conclusions. It is important to ensure your own
data is not being skewed towards the subset because when you have many data, it
cannot represent the whole set. If you are not representing your whole set, the
conclusions won’t be accurate.