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5 Challenges Of Adopting Analytics In Startups Vs Larger Organisations

Analytics is crucial for every organisation today — small or big. Adopting analytics has a direct impact on the success of companies ranging from improved decision-making to better use of the extensive data generated. While bigger organisations find themselves in a better position to adopt analytics, the same cannot be said about startups. However, there are challenges that both larger organisations and startups face when it comes to adopting analytics at some level or the other. In this article, we will discuss five such points that make it challenging to adopt analytics in startups vs. in larger organisations. 


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Availability Of Resources

Building an analytics function is not an easy task. It creates a considerable amount of financial strain on an organisation in terms of investment — setting up the right infrastructure, getting the right tools and hiring the right talent for the analytics team. While it is easier for larger organisations to readily deploy resources, startups may it find it challenging to get all the right resources at the right time. The capital allocation and funding for these two varies significantly which ultimately shows up in slower adoption of analytics tools and models. 

Cost Of Hiring The Right Talent

The cost of hiring the right talent also goes up significantly if the companies are looking to hire the best fit. Since startups may have limited funds to get the right talent, they often tend to prioritise hiring for profit-generating like in operations, marketing and sales, rather than data scientists. Whereas hiring data analysts or scientists is easier for larger enterprises. A lack of good talent ultimately slows down the analytics adoption in startups compared to larger enterprises. Also while it is easy for larger organisations to attract talent, startups may find it challenging at some level. 

Quality Of Data And Data Collection

Startups may struggle with accumulating the right kind of data and that which is also good in terms of quality. Even if they have large chunks of data, they may face problems while interpreting and making sense of data they have in their organisation. While modern technology makes it easy for startups to capture data to some extent, just posing data doesn’t mean that they can use it to improve business, which may not be the case with larger organisations. They have large resources of data and a separate team that deals with cleaning and de-cluttering it making it much easier for them. Startups, on the other hand, face challenges such as collecting qualitative data about their customers, converting qualitative data into quantitative data, collecting accurate data, and if the data collected is clean and standardised. 


The one point where startups have the upper hand than larger organisations is agility. Startups have the power to move quickly and easily to adopt newer initiatives in the organisation. In larger companies, the organisational structure and hierarchies make it harder for them to manoeuvre any changes. Much time is spent in democratising the ides and consensus-building. Agility comes as a major challenge in the larger organisation as opposed to smaller firms where it is much easy to bring about changes regarding analytics adoption or incubate newer initiatives. Few decision-makers make it easy for startups than larger organisations. 

Access To The Ecosystem

It goes without saying that large and well-established organisations have the calling card and access to the ecosystem players. Access to the large and small; local and global player makes it easy for larger organisations to collaborate with them and form partnerships to work towards a larger goal. They will easy access to analytics ecosystem compared to startups making it easier to deploy analytics solutions.

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Srishti Deoras
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.

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