A Guide to Interpreting Data and Choosing the Right Modernization Service

Here is a guide that discusses the best ways to interpreting data and choosing the right modernization services for your enterprise. Learn how you can modernize your data visualization and data integration processes.

The Baseline

The right application modernization service can only be administered to an enterprise when the enterprise release management is up to par. It helps the organization flourish by making the invisible work feel tangible to customers and clients alike. There arises a better understanding of workflow amongst different teams involved, and this also gives a better perspective on the nuances of the overall release cycle.

In a post-truth era, infographics can be interpreted and misused in various instances. There are many ways that misleading graphs can deter the right modernization process to be adopted. The red flags that show up when interpreting data can be avoided from the get-go.

Let’s learn about some red flags in statistical analysis and how the misuse of graphs can be avoided:

Red Flags in Graphical Representations

There are a number of things that needs to be kept in mind while reading graphs. Below are some ways graphs can lead to inefficient release management cycles:

• Truncated Graphs: In this graph, the baseline or the axis of the graph is completely omitted or tweaked to change the end result. This tactic is usually used to make one group look better than another. In the data visualization world, it is understood the truncated graphs can make something that’s not of much significance to look like a massive difference.

• Incomplete/Tweaked data: Cherry-picking data is the obvious misstep to effectively creating a visual representation of hard numbers. It skews the viewpoint one might arrive at, if presented with more accurate data. The lapse of incomplete or unfinished data usually occurs when one of the axes represents time. The time range needs to be made sure that it captures events right before and after the time duration under question.

In the above graph, only the first half of the year is accounted for. So, it’s a surety that the temperature has only raised dramatically.

• 3D Graphs: These graphs can throw the viewer off because of the skewed proportions it can put when viewed from different angles. 3D graphs have a likelihood of making things look larger or smaller depending on the right angle.

In the above example, it is clear that number of deaths for A is higher. The 3D graph makes it look like A & B are of equal proportions when that is clearly not the case.

The Hurdles to the Right Enterprise Release Management Strategy

Enterprise releases usually primarily focus on getting their product or service into the customer’s hands. This provides rightful feedback and improves revenue. However, often teams encounter certain common hurdles while implementing an ideal enterprise release management strategy. Let’s see what these hurdles are:

• Extensive Lead time in Customer Testing: There are two kinds of lead times: total lead time and the lead time taken between the deployment of a product and customer testing. The first kind of large lead time can lead to roadblocks in earning early revenue and ultimately delays obtaining customer feedback.

The latter kind of lead time inevitable gets extended due to features being coded and deployed in production to the time to when the customer can actually test the feature. This leads to slow deployment frequencies and lengthens the release cycle as a whole. The solution to this is to norm with the customers and find out how usability testing can be made a collaborative effort. This can be done by teams prioritizing certain features for early releases and merging faster and slower teams to work more efficiently together.

• Effective Communication: Marching a development team towards a deadline with no clear inputs from the team itself can be a huge injustice to the expert input. Arbitrary deadlines come into picture where there is no clear understanding of the resources available and the desired output. Ineffective communication is a major obstacle as it increases time consumption, slows down the testing process and affects the overall QA implementation- this inevitably affects the quality and increases the delivery time.

How to Effectively Interpret statistics and pick the right Modernization Service?

There are numerous ways how statistics can be misinterpreted. There can be honest mistake such as faulty polling or flawed correlations, and the list can go on to truncated graphs, misleading visualizations, cherry picking data etc. Merely knowing of these red flags can help in not skewing the end result of any analysis for an enterprise.Although statistical data can be misinterpreted and misused, it can also ethically garner market value in the digital world. The right statistical interpretations can come help pave way to adopting an effective and comprehensive enterprise release management strategy.

Application Modernization involves updating old software for newer computing methods. These upgrades include new frameworks, infrastructure and languages. This requires teams to come together and have a more unified approach to statistical analyses. Contributions made by individuals and teams can be better measured when the right release management tools are used.

There is no scope for long lead times and hurdles to be left unattended. When technology and efficiency are given equal importance, the right digital transformation journey can be put into practice. Software-as-a-service (SaaS) solutions help with running the most recent version of application without the need for updates or rearchitecting- leading to significant cost savings.