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Turning Source Data into Insight

Data alone has very little significance, and actionable insights cannot be materialized until the data is transformed into meaningful visualizations. Research must be conducted to find viable source data. This data can then be extracted from one or more repositories and combined in visualization tools like Tableau to garner meaning. The selection of appropriate data sets from the various sources is imperative in painting a visual tapestry that will produce useful insights for your organization.

Advancement of state-of-the-art visualization tools allowing massive amounts of raw data available, also known as Big Data, to be tamed and converted into useful information is an exciting current trend. These visualization tools are becoming easier to use which will allow organizations to gain a competitive edge over their adversaries. The available Big Data and advanced analytics could have a major impact on everyday operations.

A few large companies such as Amazon and Google have achieved this realization, where data analytics is built into their infrastructure. The average company has achieved very little success in using Big Data and data analytics to improve their bottom line. Some companies have had success with data analytics, but it is usually limited to small segments of their organizations.

The apparent link between source data and insights in the average business is adoption. Change is resisted by most; a major paradigm shift needs to be made to take advantage of Big Data, data analytics, and the insights that evolve.

Companies that want to adopt state-of-the-art data analytics will need to develop a transformation plan. This plan needs the backing of senior management to be successful and to place emphasis on data; most importantly, the transformation plan must address cultural challenges and provide training for all frontline management. If this plan does not meet these requirements strategically, the transformation will not be embraced and will fail.

As this transformation plan is being implemented, there will be numerous challenges in gaining any large-scale benefit. Many managers do not have an adequate insight of what data analytics can do for them and will have problems in justifying additional investments in their infrastructure to accommodate new technologies. Due to the lack of knowledge and confidence in what analytics can do for them, there will be a hesitation of its use. Also, traditional decision-making protocols that require multiple approving authorities do not allow for the expenditure of resources necessary to utilize advanced analytics and the automation necessary for its use.

If you notice your organization is having problems in its efforts to adopt data analytics, you can set an example by using them yourself. Your use of state of the art analytical tools on a regular basis should give your associates and coworkers a higher degree of confidence in their use, which will eventually provide better insights from generated data.

Analytical tools by themselves are not enough to overcome your organization’s resistance to change. Upper-level management needs to embrace the advancement in automation by engaging data analytics in core business areas and making this a goal throughout the organization. The adoption of data analytics might require the redesign of many jobs; adapting the workplace in the use of data analytics on a regular basis should allow the organization to gain large-scale benefits through garnered insights.

Traditionally, management buy-in on new concepts and techniques is a challenge. Analytic champions need to be found and recognized; these champions can demonstrate the potential of analytics by developing visualizations from internal and external data sources that will demonstrate actionable insights previously overlooked. These champions need to promote and sell their analytic visualizations to generate management buy-in.

Most data analytics efforts fail before any benefits are realized. Due to past failures in gaining returns from analytics, senior managers are often hesitant to invest in this new technology. Also, there may be a lack of confidence that analytics can enhance decision-making. Lastly, many traditional business practices are entrenched barriers that prevent the use of advanced analytics. Today’s organizations need to be cognizant of these problems and develop a new paradigm that will allow the use of data analytics to stay competitive.

The link between source data and insights is not as simple as it may seem. Understandable information is generated from source data with the use of various analytical and visualization tools. This information is then examined to provide us with insights; this sounds simple, but to make this work, the mindset of everyone within the organization needs adjustment.

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