Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Chapter 14: Crucial factors for data
analysis 
 
The use of big data analysis is becoming standard procedure in
organizations as more companies include this practice as a part of business
activities. Companies that are progressive are investing funds in big data
analysis because this business intelligence has proved to create amazing
outcomes by rationalizing information to increase productivity and
revenues. However, in many cases, organizations have shown no benefits
from installing this system. Questions arise about whether big data still
holds the promise that was expected. In principle it seems beneficial, while
in practice, results were not as precise as anticipated.  In cases where the
information delivered   was incorrect and useless, the system has been
abandoned.
 
A 2007 study revealed that a third of companies did not have budgets for
intelligence projects. 31% of these projects were discontinued by
companies, while 17% functioned so poorly that the judgement and
existence of these companies came into question. Certain companies have
made 100% return on investment by using business intelligence. They were
able to reduce overheads dramatically and customer and revenues
increased. Other firms had only a slight return on investment while the
majority have seen nothing but losses caused by the system.
 
Little research had been done about the factors that contributes towards the
success or failure factors of business intelligence. The success of business
intelligence projects depends on the ability to assimilate large amounts of
information into a system and have the analytical facility required to
examine this information correctly. Important measures to determine the
success of any BI venture are information assimilation, value of data, ease
of accessibility, diagnostic ability, application of analytics in corporate
activities, and making systematic judgements.

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Before investing in business intelligence systems, questions about important
achievement aspects that indicate project success must be answered. Key
drivers towards accomplishment include communication, project
management, vendor and customer relationships, and user participation, to
name a few categories. It has been observed that smooth system usage,
organization elements, technical features, and organization procedures are
all significant issues that relate to the success of business intelligence
projects. Since it is difficult for businesses to concentrate completely on
these many aspects, it is necessary to find out which of these is most vital
and then focus on these major features.
 

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Support by top management   
 
Experts are of the opinion that the success of business intelligence projects
depends on committed organization support, pliant and interactive technical
structure, change management practices, solid IT and BI control, and a
united BI and business system. Consistent support from top executives
makes it easier to secure necessary operating resources such as funding,
human skills, training, and other necessary requisites throughout the
implementation process. The success of any business intelligence system
depends largely on the support it receives from top management in the
organization. This has been noted as a vital aspect that effects success or
failure of such projects. Without forward thinking vision and backing, the
project would have little or no chance of succeeding.
 
It is the organizational department heads must support this sector by
providing necessary resources for data analytics to function at full capacity.
Without dedicated support from top management, the project might not get
the recognition it needs to be a success. This happens because users tend to
follow to the attitude of top management and will usually accept a system
backed by superiors. Sufficient financial allocation and quality human
resources will go a long way in establishing an efficient big data system and
here top level support will make a huge difference to the BI system and its
success.
 

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Resources and flexible technical structure
 
During the initial installation of a big data system it is important to ensure
that all the components are able to adapt to changes. This is an expensive
time consuming project so hardware and software required for analyzing
the huge volume of information should be flexible enough to adapt to
current requirements and expand with future growth of the system. Initially
it takes much time and money to support such systems, but with sufficient
investment the benefits are sure to follow. Having access to big data
empowers the users and organization to make accurate decisions. While
cutting out conflict, errors and incorrect feedback, and false management
information, the system creates improved situational awareness which helps
to direct business. Setting up a successful business intelligence operation
can transform an organization.
 
When not enough time or money is invested in the big data system,
problems could arise very quickly.  It is not uncommon to hear of data
warehouses not able to function properly after being installed. The main
reason why many business intelligence applications fail to deliver
expectations is simply because the technical structure put in place is
inadequate due to insufficient investment. Exorbitant costs, lengthy
timescales, and cumbersome user requirements which keep changing, make
it imperative that sufficient resources are made available from the start
because this is an expensive project that will expand, requiring even more
time and funding as it grows. One way of keeping costs down is to select a
specific area of business to focus on first, and then add another important
area and another. This is one way of ensuring your BI project’s success
while gaining momentum and credibility.
 

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Change management and effective involvement 
 
Change management guides the manner in which individuals are prepared,
equipped, and supported in order to adopt changes that will propel
organizational success and productive outcomes. The success of any BI
project is directed by the rate of executive involvement at the beginning of
its implementation. Business intelligence operations work better and faster
with a highly motivated level of management involvement. It often
becomes essential to change company processes when business intelligence
projects are installed. This is because BI solutions must unite with the
organization’s strategic vision and the project must be scoped and
prioritized to concentrate on the organization’s best opportunities.
 
New innovative ways of working are imperative for enterprises that require
an effective BI system. It is imperative for the execution team to understand
the processes of the system. Employees must be encouraged to accept new,
advanced ways of doing things.  If there is resistance to change by any
people who are responsible for BI functions, they may have to be changed
as well. Change management is required for data users to accept software
into their workflow. It delivers a support structure for employees to move
from their present state to a future state which is more business friendly.
 
For change management to be consistently and effectively applied to
initiatives, leaders should have the ability to guide their teams through
changes. Employees should be able to embrace change more easily, quickly,
and efficiently so that organizations are then able to respond speedily to
market changes and employ strategic initiatives and technology with less
effect on productivity. This capability does not take place by accident. It
requires a strategic methodology to implant change management throughout
the organization. Change is good as it creates new opportunities and widens
perspectives and horizons.
 

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Strong IT and BI governance
 
The substandard quality of the data supplied to the analysts system is
among the one of the main reasons for the failure of business intelligence.
This data is gathered by the operatives, and systems to ensure quality of this
collected data must be installed in the system to guard against inferior
input.  Only then can the operator produce a satisfactory study of the data.
Reliable business rules and processes must cover an organization’s
information assets. IT and BI data sources, increasing complexities,
operational intelligence, and information diversity creates an environment
that requires consistent and thorough data management strategies.
 
Surveys have revealed that corporations who have a good strategy to govern
their information also succeed in governing their business intelligence.  
Data security and governance at every level is crucial. Strong data
governance should encompass all data assets across the organization to
create a cohesive view of information and provide a way to manage
inconsistencies and potential data quality issues as they arise. Strong IT and
BI governance generates efficient data management and efficient
information, supporting automated analytics and broader business insights,
leading to superior analytics.
 

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Alignment of BI with business strategy
 
Successful business intelligence initiatives are always aligned with the
organization’s business strategy. Business intelligence initiatives that are
not aligned with organizational business objectives fail. Where there is a
struggle to align technology approach to BI with specific business goals and
objectives, solutions are delivered that fail to meet business needs. It is
important that the organization’s general business strategy and BI are
aligned in order for the data system to be effective. When the BI department
does not have a transparent, understandable business policy, it becomes
hard to control and drive big data analysis towards the company’s amplified
productivity and increased overall returns.
 
High level alignment is achieved by understanding the company’s business
strategy and integrating this plan with data usage elements, metrics,
dimensions, lists and values, and patterns of use. Business intelligence
alignment with business strategy supports organizational processes and
enables analytics efforts to meet business needs. It enables agreement on
business vocabulary and definitions, manages costs and risk in reporting
and BI methods. Solutions are designed that meet customer needs and
deliver value, and partnerships may be created to advance shared
information. Alignment of BI with business strategy leads to improved user
reception and intensifies the benefits received from business intelligence
investment.
 

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