There is not a single clear-cut pain point but many related technology problems that we encounter over and over from businesses trying to leverage analytics to drive continuous, data-driven improvement across the enterprise.
Agilytics has compiled some of the major problems as follows: -
1. Insight from the data is not the end of analysis
Many analytical products promise to convert data to “insight.” But what is the insight? Many times it is a static report or an interactive dashboard with attractive graphs that let you visualize a ton of data any way you want. This does not add immediate value to the business. Businesses do not make investments in analytics because they need insight.
What matters is the recommendation for actions to be taken after getting the insight.
2. No set scale of Data Collection
Some businesses collect high volumes (terabytes) of data from machines, transactions, and beyond, but many applications, tool-set and methods can not keep pace with the volume and speed of data collection.
3. Incomplete solutions
Much of the analysis is of the old data to get to know what happened and why, which is the descriptive analytics. It is more helpful to know that customers who are male, have purchased a particular product, and have called the contact center three times, churn within the first month unless offered a promotion. If you offer a promotion to customer meeting this “criteria,” you will reduce churn – that’s the useful takeaway.
That is where predictive analytics becomes critical. It gives the ability to recognize what events, transactions, interactions are likely to lead to a particular outcome.
4. Steep opportunity cost
Many times data analytics requires experts to spend lot of time in cleansing, querying, coding, and modeling before real answers are produced and changes to strategy are deployed. Many companies collect a high-volume of data e.g. terabytes on the daily basis. By the time these recommended strategies are produced, the scenario changes and solution becomes obsolete.
5. Accessibility of Data Analytics Software Tools
Most analytics software tools are coding-heavy data science tool-kits like R Studio to drag-and-drop studios, which require users to have significant expertise in data science, statistics, coding and software to transform the data and develop models. But the users who need to leverage data are business managers who are without this expertise.
Your Data Speaks. Agilytics will help you listen to your data and grow your business in a better pace and in the correct direction. Agilytics adopts Agile methodologies to create unmatched software solutions for customers’ delight.
The team Agilytics consists of IIT and IIM alumni with in-depth knowledge and experience of Statistical concepts and programming. We are on our way to become preferred partner for executing projects for developing data models, data analytics, GIS, AI and Location Intelligence.
Contact us now at firstname.lastname@example.org for help.