Since enterprises are digging for the data, it may be reason to name it!
There are many process are driven by data!
Data is used to take decisions!
Many more incidents and claims are happening in the business world.
What is reality? Or What is actually happening in this data world or world of data?
Many brands invested into these tools and awaiting for the return on investment. ROI.
Yes, data is truly big and happening word, however, it is not defined clearly!
There are many pain points that should be addressed.
There are many analytics options and one must put a big effort to choose.
There are several technology related problems that we hear over and over from brands trying to leverage analytics to drive continuous, data-driven improvement across the enterprise.
In this post let us discuss the pain points (No specific order!)
1) Single way process.
Frequently, analysis is undertaken in an ad hoc way—a Single way process used to find value. But enterprises wanting to improve the business continuously need analytics to be systematic and repeating. The analysis can not only be a liner.
2) No value adding “Insight”.
Many data analytical tool promise to convert data to “insight.”
What is insight?
All too frequently, “insight” is a static report or an interactive dashboard with beautiful graphs that let you slice-and-dice a ton of data any way you want.
This does not add immediate value to the business. For a business owner what matter the most is, someone needs to expend effort to make sense of it all, and figure out what actions should be taken.
Enterprises aren’t making investments in analytics because they need insight. It’s a means to an end, and the end is “answers.”
Business owner want answers-specific, practical actions to take to improve the metrics they care about.
3) Too big to handle by the chosen tool
Enterprises are collecting high volumes (terabytes) of data every from machines, transactions, and beyond, yet many tools and methods can’t keep pace with the volume and speed brands are collecting. This actually affects entire operation of the IT and its’ infrastructure. The collected data is not going to be an asset, it actually creating liability.
Enterprises should aware of this and they have to choose right data collection methods and best suited analytical tool.
4) The excellent Post-Mortem report.
Much analysis on offer today is a post-mortem report and many times it stinks during discussion itself. The analytical tool look at old data to determine what happened and why (descriptive analytics), in order to make beneficial changes in the future.
It is helpful in few quarter of the business may be to know that customers who are male, and have a particular product, and have called the contact center 3 times churn within the first month unless offered a promotion. If you offer a promotion to customer meeting this “profile,” you will reduce churn – that’s the valuable takeaway.
But to capture this value, you have to know when you’re talking to a customer with this profile, so you can make the offer. And you need to know all the thousands of other profiles that lead to churn. That’s where predictive analytics becomes critical. It gives you the ability to recognize what events, transactions, interactions are likely to lead to a particular outcome—such as churn—and identify them as they’re happening so you know you need to act and can do so at the right moment.
5) The reports or answers from the analysis are biased & incomplete
Most analysis requires humans to query data. The analyst doesn’t have sense of business.
The results of the analysis illustrate only the questions the analyst or data scientist thought to ask, ensuring that answers are biased and incomplete.
These human intervention kills many projects as the business owner is not getting “insight” and blames the tool.
6) Deliverables are stale. Not useful. Costing high on opportunity.
The terabytes of data are not clean. Not complete. Not of structured. Not in form. Useless and junk are more than useful and tangled together.
Most analytics still requires experts to spend months and even years structuring, cleansing, querying, coding, and modeling before real answers are produced.
Then analysis is carried over for the changes to tactics are deployed.
By the time these answers are produced and new tactics are deployed, they’re stale, even obsolete because of the long cycle times.
This is because competitors, customers, and environmental pressures change the facts on the ground every second, minute, day, or week, depending on your business.
When businesses can’t analyze incoming data quickly enough to respond to changes in the market, the opportunity cost is huge.
7) Long and High energy and resources to set up.
Many analytical tools demands, intensive and resource consuming, efforts to set-up. Moreover the complexity of the data and interface with tool takes huge consumption of human hours and days and weeks for cleaning and querying, modeling and experimentation along with deployment. This tires the business men and the users.
8) Non-structured data therefore unused data.
Now a days the data is not only a digit or numerical. It is in the form of mail, message, social media posts and phone calls. These are all real data mines but rarely digged!
Some analysis methods and tools only analyze numerical data, and not categorical values. Most organizations aren’t joining related data across silos to try to understand how variables captured in one department’s system combine with variables in another department’s system to drive a KPI up or down.
Brands have invested significant resources in wringing value from data, but many are only tapping a small percentage of data available to them, leaving enormous value on the table.
9) No democracy to access the data
Most tools require users to have significant expertise in data science, statistics, coding and software to transform data, choose and develop models, etc. The access to the data and analytical tools are available only to a department (IT) and they are not exposed to business routines. Actually they never needed the data! They hate it.
But the people who need to leverage data are department managers—users without this access to the data and expertise. This divide makes the organisation to stick to their regular or old methods instead of getting into “data oriented” organisation.
10) Cost Benefit Ratio.
Installing tools and software packages is complex and takes time, and the set-up required to get started creates a long lead time to value and benefit. But enterprises want to get started right away, and many can’t afford to wait.
The cost of set up the tool and get the data into the analytical ambit is critical to enterprises to get the benefit.
11) Resource crunch.
Many enterprises are affected by the attrition in their IT department or this analytical department. This adds more effort and cost to the organisation to train and on board the resource to perform.
What are your biggest pain points?
We at Dataception understand the customers’ (your) pain points. We Solved most of them. (Only one in particular – the attrition due to management or leader is not solved by us!).
We make the Analytics tool work for you in “Hours” not even days.
Let us know your pain points.
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