Insights

The key to our success….

In our company, we face the normal everyday pressures of businesses. We always work hard for our clients and are continually seeking ways to improve. We don’t let ourselves forget that we are humans as well. We get excited and happy when our ideas become reality and we get frustrated and stressed when we just can seem to find the answers. So how do we overcome periodic downturns of creativity? Continue reading The key to our success….

STOP! – Understand and Apply Growth Curves Correctly

Growth curves are a critical part of many different disciplines including the physical disciplines, however, many business,  with the exclusion of financial companies, tend to neglect growth curves due to the preference of simpler, easy to implement linear lines. They are often misunderstood and when incorrectly applied,  lead to very divergent results. If used properly, however,  they are a great way to understand long term behavior of business activity. This article presents a brief analysis of a few different curves and compares them to a linear approach and further examines their application.

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Top 3 Reasons Why Analytics Projects Fail

Analytics projects can be very complex and require an appropriate level of expertise. The various stages of an analytics project incude determination of goals, collection of data, cleansing of data, statistical analysis, and presentation of findings. According to Gartner over half of the analytics projects fail, which is determined as a project that fails to meet its stated objects and/or runs over budget or over the stated time.

We have identified the top three reasons why analytics projects fail and it might not be what you think.

1) Lack of a Senior Sponsor

Projects lacking sponsorship of senior leadership have the highest likelihood of failure since they aren’t given the requisite attention of other projects. Disconnected leadership fails to identify or recognize the valuable insights provided by well conducted analytics projects. Further, this dissonance resonates to analysts and data scientists who may not feel their work is valued or may not feel their efforts will be realized and thereby limit their efforts and scope of results. Senior sponsors should be C-level or high level decision makers in the strategic business units, and it is incumbent upon analytics leaders to obtain the necessary sponsorship and support for their teams.

2) Lack of a clear question to be answered

Its the age old question of what are we doing and why for starters. Analytics projects require the inquisitive nature of business units to fuel the analysis. Understanding the nature of the data, and having a clear objective, solidifies the work effort and creates a less amorphous outcome. The question should be business oriented and ask for answers critical to the business such as “Who are our most valuable customers?”, “Which customers are more likely to purchase the higher level product”, and “What happens if I increase/decrease my price and how will it affect long term customer retention”. When these types of questions are asked, analytics becomes a tool empowering business leaders.

3) Lack of reasonable action

“Action is the foundational key to all success”-  Pablo Picasso

Your business units and senior leadership must be willing to take clear and decisive action based on the results provided by analytics projects. Failure to do so results in missed opportunities and complacency. “Let’s increase our price and analyze the results.”, “Let’s focus our latest marketing effort on the highest value customers”, and “Increase our communication on customers who may be ready to quit based on the results” are clear actions from results. The absence of these actions or indecisiveness becomes a clear indicator of a lack of trust in the data, analysis or analysts, and leads to failure.

Action should follow the questions from senior leadership, who are willing to trust the data and analysis as a compliment to the institutional knowledge of the team entrusted to execute on the directions. Analytics projects provide insights, they are not the end but rather a means to an end requiring buy-in and trust at all levels.

Machine Vs. Operational Learning

Today’s economy, led by advances in changing economic cycles and communication, relies more on data science. Big data techniques, a significant component of data science and business intelligence, are utilized to harness vast amounts of data quickly for analysis. Increasingly, the volume and availability of granular data, coupled with highly specific and powerful analytical tools such as R and SAS drive organizations toward making more accurate predictions with the prospect of increasing sales and generating organizational efficiencies. These predictions help enable efficient supply chains, driving down costs for producers and leading to more expedient delivery of products and services for consumers.

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United Airlines Passenger Incident Causing a Twitter Storm

There are dark clouds surrounding United Airlines and the ongoing PR debacle. The recent passenger incident for United Airlines has caused quite an uproar in the media. It has also created a major firestorm on twitter, with a number of comments from passengers and customers voicing their displeasure. Continue reading United Airlines Passenger Incident Causing a Twitter Storm