Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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For Companies, Data Analytics is a Pain; But Why?

By Nand Kishor |Email | Apr 7, 2018 | 13314 Views

Businesses across the globe are facing the brunt, one of huge data influx and second of increasing data complexity and of course the market volatility. To address these challenges, companies and all their verticals are turning to data-driven analytics and insights as a means to better understand their organizationā??s customer bases and to grow their businesses; and manage the increasing uncertainty up to a certain extent. 

The shift from conventional to data driven analytics is steered by technology and automation across organizations. Growth in digital technologies is enhancing the abilities to analyze more and more data, ultimately increasing the appetite of enterprises for more data, better data, advanced analytics, implementation of best practices and what not. Data analytics is the primary enabler to derive insights and reach out to meaningful truth, resulting in business growth and increased revenue.

The world is going gaga over the promise of analytics and what enterprises can attain by harnessing it, compelling brands to make significant investments in analytics tools, or analytics service providers. However, somewhere down the line it feels as if analytics is a bubble, which is likely to burst anytime. And there are various reasons and several related technology pain points which surface, as conveyed by brands trying to leverage analytics to reap benefits of data-driven improvement across the enterprise. In no particular order, enlisted are some of them:

1. Analytics is not a vaccine, but a routine workout
Companies looking out for instant solutions, usually think of analytics as a vaccine shot and undertake it in an ad-hoc way, a kind of a one-time process to find value. It should not be the case at all. If enterprises are keen on improving their businesses continuously, they need analytics to be systematic, and repetitive like a routine work out in gym.

2. Insights are just the initiations, and donā??t add immediate value to your business
USP for some of the analytic players is the promise to convert data to "insights". Organizations should understand what is insight? Usually insight is a static or an interactive dashboard, comprising of graphs enabling the slicing and dicing of tons of data, the way one wants it.

But does it add any immediate value to the business? For these insights to be really of importance, human intervention is required to make sense of it. And also to figure out what actions should be taken. You as a business would not make investments in analytics, because you want insights - would you? Unless it gives out answers, such insights are of no use. Businesses need answers, specific and practical, to improvise the metrics with immediate values.

3. Scalability
Enterprises and organizations, in that rat race have or start collecting high volumes of data  from every machine and transactions available. However, the aspect that needs to be thought about is, are they equipped with the right kind of tools or data analytics team or have they even partnered with decision analysts who can help them keep pace with the volume and speed at which the data is generated. Usually, most of them are yet to get associated, and unfortunately few of them are yet to think about taking up the data analytics approach.

4. Descriptive analytics is a post-mortem, does it really help
Data analytics offered by most of the online tools, or analytics service providers is a kind of post-mortem; a look back to the old data to assess what happened and why - just in order to make beneficial changes in the future. It really is helpful to know when male customers visited your eCommerce site for a particular product, and will get easily churned if no promotion is offered in the first three months. If you succeed in pitching a promotional offer to this customers meeting this kind of profile, you will succeed in reducing the churn as well - and itā??s a valuable takeaway. 

However; it is more than important to know when, customers with this profile, are talked to so as to make the offer. Along with this, you are also required to keep a tab on other profiles that can increase your churn ratio. And this is where predictive analytics walks into the picture. It empowers you to recognize what events, transactions and interactions are likely to lead to a particular outcome, churn - in this case. It also helps in identifying such cases while they are happening so as to enlighten you to take required action at the right time.

5. Human intervention in analytics is a friend and a foe too
Usually data analytics needs humans to query the data, and the results of the analytics exemplify only the questions the analyst of data scientists thought fit to be asked or put on paper, ensuring the answers are biased and incomplete.

Decision making depends on instincts and intuitions and it is risky as human beings are not inherently impartial. Several cognitive biases and logical fallacies do exist, and have the potential to affect decision most of the times. The best way to reduce cognitive bias is to rely on data to make informed decisions and not pure human intuitions. Data scientists should be made more responsible and cautious to stay impartial when supervising machine learning and organizations should embrace data collection from all available avenues, considering the objective of data in the first place.

6. Opportunities cost is huge; stale answers make dents 
Conducting data analytics is process which needs experts and data scientists to spend months and years in data collection, cleansing, validation, modeling, visualizing; before reaching out to final conclusions, and deploying tactics. And letā??s not forget that enterprises have been collecting terabytes of data - daily.

So by the time, answers in the form of analytics are produced and new tactics are developed and deployed, they get outdated; and obsolete at times because of the long cycle times. The reason to this is that competitors, customers and environmental pressures usually consistently keep on changing ground reality every minute - every day - depending on the nature of your business. That is why stale answers make dents that are irrevocable and opportunities cost really huge.

7. Manually intensive
The integration of analytics thought process in an organizational set of beliefs is mostly manually intensive. There are a lot of whys and whats, and the curiosity is welcome; but overdoes of it at times is time consuming and proves really costly. And after all this, the actual analytics is usually time consuming in terms of human hours, days, weeks or months of querying, coding, modeling, experimentation and deployment of course.

8. Numerical data is analyzed, but what about categorical values 
To analyze numerical data for analytic solution providers or any of the online or licensed analytic tools is like a cake walk; but what about categorical values? Most of the organizations fail miserably at aligning relevant data across siloes to try and understand how information gathered by one of the departments or its systems, combined with variables of another department to drive performance and efficiencies up or down. They also fail at tapping into the unstructured data in free form text from sources like email, social media, and calls.

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. Users without expertise
Most of the analytics tools, from coding-heavy data science toolkits to drag and drop studios, need users to be well equipped with significant expertise in data science, statistics, coding and software to choose and develop models, transform data etc. However, the irony is that people who need to exploit data are department managers with less or no expertise of it.

10. Increased lead time to value
Installing software packages and analytics tools is a time consuming task. The kind of set-up required to get started increased the lead time to value. However; enterprises want to get started NOW, and still there are many who cannot afford to wait.

Source: DSC