Tag Archives: DataAnalytics

Knowledge and Data – The brains of a business!

All successful businesses, big or small, have one thing in common – Extensive business knowledge. In big organizations this knowledge can reside in entire teams, or units. In small organizations such knowledge is restricted to the people who founded the business, and maybe a few key employees that drive company strategy. In either case, scaling and sustaining organizational growth might be difficult, if the knowledge discovered, collated, and curated by the team is not stored for future reference. And that is why it is important to have Knowledge Repositories.

Before going any further, let us understand what knowledge means to a business. Business knowledge is a sum of skills, experiences, capabilities and expert insight, which you collectively create and rely on, in your business.

Knowledge can exist in many forms, but can be broadly categorized into:

  • Tacit knowledge – Personal know-how or skills rooted in experience or practice (Eg. Skill, Competency, Experience).
  • Explicit knowledge – Articulated knowledge recorded in documents, memos, databases, etc.
  • Embedded knowledge – skills and understanding locked in processes, products, rules or organizational culture (Eg. Informal Routines, Codes of Conduct, Organizational Ethics).


Your understanding of what customers want, combined with your workers’ know-how, can be regarded as your knowledge base. Storing, searching, accessing and using this knowledge in the right way is together known as Knowledge Management (KM).

At its core, KM has important implications on decision making in an organization. Effective KM supports the process of decision making and strategic planning and makes it possible to create, transfer and apply knowledge at different levels in a coherent and productive way. 

All of this is sounds great, and we can look up to major organizations such as Microsoft, Amazon, etc. for how they manage knowledge. However, implementing this in small businesses might create some challenges.

To do it right, a small business should

  • Start with thorough research to find the right tools for knowledge management
  • Implement these tools, without shying away or getting intimidated by new technologies
  • Thorough documentation goes a long way in Knowledge Management such as developing SOPs and guidelines of workflow.
  • Lastly, simple creative measures such as mentorship programs and discussions ensure effective flow of knowledge sharing and develop awareness amongst employees.

All the efforts of Knowledge Management are pointless if it is not safeguarded and backed up. A surprisingly unsettling statistic shows that 60% of companies that lose their data will shut down within 6 months of the data disaster. In addition to replacement time and irretrievable data, catastrophic data loss can destroy client confidence, leading them to take their business elsewhere. Retrieving the data requires an embarrassing explanation, and lost data could even lead to lawsuits.

Given the risk loss of data poses, it is important for every business to have a reliable backup solution. If your business has physical servers, backing up these entire systems are critical. As a small business owner, this should not be a big task once you have a proper plan. Backups can be created using two basic methods: file level and image level. File level is perfect for backing up files and folders on your file server. Image-level backups are perfect for when you want to protect an entire system at once.  Backups can be done in full, incremental or differential manner. A right backup strategy would also include backups such as Cloud Backup, Encryption of Data In-Transit, 3-2-1 Strategy, and Testing Backups.

Knowledge Management helps you run your business more efficiently, decrease business risks and exploit opportunities to the fullest. So, treat and safeguard your organizational knowledge as carefully as you would a sack of diamonds. Because it is every bit as valuable or more.

Reaching Data Saturation

Data saturation is everywhere. We’ve often had the belief that more is better; however, that actually isn’t true in the case of data.

Why do hypermarkets keep chewing gums and candies at the cash counter? How do coffee chains manage to have two cafes on the same lane and still be profitable? How do online searches draw advertisements of the same products on your devices? Most of this is not conventional wisdom, it’s use of data, which companies across the world are obsessing about. Data analytics, for many, is the holy grail to drive user demand and revenue. 

To put the data deluge in perspective, Google processes 3.5 billion searches per day, Snapchat users share 527,760 photos, 41.46 million people watch YouTube videos, Instagram users post 46,740 photos, and 456,000 tweets uploaded on Twitter. India generates a subset of this consumption, but the numbers are bound to be massive.

India is the second largest internet nation with close to 400 million internet users, many of them on smartphones, only second to China. Access to social media, Google search, entertainment, among other things, on the palm generates data that is dissected to get desired outcome. 

Add to that, hyper-focused targeting and segmentation to define niche audience segments. But is the entire stack of data important and efficient? 

Data mining became an important aspect in the late 1990s,  but as new concepts like big data, and technologies like artificial intelligence and machine learning surfaced, it opened-up the Pandora’s box. “There isn’t a thing as too much or too less,” says Pranay Agrawal, CEO of Fractal Analytics, a Mumbai- headquartered analytics service provider. Citing the example of a leading diagnostics chain for medical data, he says, “If the prediction accuracy improves by even 0.5 per cent, millions of additional data is worth it.” Size doesn’t equate usefulness. Many believe that data isn’t about size but relevant attributions and metrics  — so, more the merrier. But there are different approaches. 

Abhishek Ganguly, Managing Director of multi-channel sportswear brand Puma, believes in a “business objective first” approach. “Instead of looking at ways to collect and mine data, we start with business objectives. Then we create platforms to collect data,” he says. There are many, like Ganguly, who believe in the business- first strategy. “Business-first framework should take into account use cases, data sets, data collection, data preparation, learning and intelligent actions,” says Pramad Jandhyala, Director of Finance and Human Capital at digital analytics firm LatentView. 

But there are segmentations to this approach, one them being the size of the organisation. “For large, established enterprises, data-first strategy drives innovation. But for start-ups and SMEs, setting up and aligning business objectives first should be the priority,” says Arijit Lahiri who runs QuoDeck’s, a game-based mobile learning management system.

Hero MotoCorp, Mahindra Group, Reliance Industries, and Baja Auto are spending millions to build data-driven enterprises. Others like Amazon, which has the customer at the center of its business model, has constituted a team of data scientist, A1 experts, and data analytics models. For them India is no different.

Amazon uses AI and ML to analyse huge chunks of data in various fields — from improving address quality to ranking of deals, and from improving catalogue quality by finding missing descriptions in titles to weeding out inappropriate images. The good part is that Amazon has a lot of user-generated content from product searches, buying patterns, and search. All this data is analysed to figure out what customers want. It doesn’t stop there, Amazon takes its findings forward to its entertainment platforms: Prime Videos and Music.  It has been at it for ages. But every firm is not Amazon and every CEO is not Jeff Bezos. Even with the right approach, money-inflow and business alignments,  a digital-data strategy can fail. Making sense of data is the biggest challenge. With colossal amounts of data being generated every day — 2.5 quintillion bytes — we are bound to get lost in the web jungle. 

QuoDeck’s Lahiri says that data without a hypothesis is pointless. “Before collecting data, one needs to have a hypothesis in place. You might know the answer but if you don’t know the question, it won’t make sense,” he says, alluding to the bottleneck in the analytics industry. 

“Forward-looking approach that relies on data-based intelligence, trend analysis, forecast modelling, and predictive analysis is what people need to obtain success in today’s dynamic market. And only accurately collated, actionable insights can give them that, not massive amounts of raw, unfiltered information,” says James Giancotti, Co-founder and CEO, Oddup, a data-driven research and insights provider. 

Oddup’s chief operating officer and Co-founder, Jackie Lam says that just analysing past trends alone won’t help, because the market is constantly evolving. But all of this cannot happen without making data-analysis any company’s culture, like in the case of Amazon. 

“Creating an organizational cultural can solve the issue of failure. And the culture should follow the top-down approach,” says analytics academician Dakshinamurthy Kolloru, Founder President & Chief Mentor of International School of Engineering. 

This story has been written by Nishita Chandak from Times Group

This article was first published in, The Times of India