Data Analytics in StartUps – A Secret Sauce to Success

Introduction

Data has become an integral part of any modern business whether it is tech driven or non-tech. All kinds of business from manufacturing to healthcare are heavily dependent on data in many ways. Right from building a platform for the day to day operations to understanding the behavior of customers as well as machineries, data is present in every aspect of modern business.

Large enterprises and conglomerates have been using ETL Tools and data analytics to effectively run their business for a long time. This has also helped them serve their customers in a much more effective way.

However, for small businesses and startups, the scenario is a bit different. One of the recent studies by OnePoll reflects that 56% of small businesses seldomly look at their business data and 3% of the small businesses never look at it at all. This could be broadly attributed to two factors.

• Lack of knowledge about data analytics among the decision makers
• Absence of tools which suit to their business requirements

The possibility of the second factor seems more true as modern businesses have dynamic requirements which keep on changing as the business grows. And conservative tools are either incapable of adjusting themselves or are extremely costly to use. This is where platforms like Sprinkle come into the picture. Over the last few years Sprinkle has worked with fast growing startups, which have now turned into unicorns and empowered their business with the no-code data analytics platform.

Role of Data Analytics in Startups

Every startup is born as an idea in the founder’s mind, which he believes can revolutionize the world and solve a particular problem. But data suggests, only a few startups actually survive beyond the initial phases. This is because the idea needs to be executed in a proper way and it needs to be flexible enough to adapt to the ever changing business environment. Startups which are proactive in adapting to change are the ones who win the race in the long run as compared to reactive startups. And for this decision, data plays a major role in understanding trends and making decisions accordingly.

This role of data and analytics can be further broken down into the following:

1. Setting Up Key Performance Indicators (KPI) : Data analytics plays an important role in setting up business goals. Without data, it would be very difficult to set benchmarks for the start up, set up goals and work towards achieving those goals. This is where KPI first platforms like Sprinkle play a major role which keeps the business KPIs at the forefront of any analysis.

2. Enhanced Decision Making : Relevant data must be available to every key stakeholder and decision maker in the company. This empowers them to faster decision making which helps the business in the long run. But, if the start up has a data analytics stack which requires business users to reach out to the data team for each and every requirement, that becomes a bottleneck and the business suffers.
Here, tools like Sprinkle come handy as it enables business users to run their own analysis and enhance decision making.

3. Targeted Marketing : As mentioned earlier, businesses who are proactive in adapting to change are the ones who survive till last and are the winners. This holds true even when it comes to understanding customer behavior. Surviving the customer what he needs at the right time results in a win for the business. This understanding comes from analysing the marketing data with which the potential customer has interacted with in the past.

For example, if a person wants to book a flight ticket from Mumbai to Bangalore, and has been buying tickets for the same route every quarter, a promotional campaign in the next quarter providing some discount or some facilities may result in the customer win.
But, most of the time this marketing data is fragmented across systems and analysing them to arrive at a conclusion becomes a challenge. This is where Sprinkle has been working with businesses to collaborate their marketing data at one place and then analyse the data to arrive at business decisions.

4. Increased Efficiency in Operation : Analysing data from time to time, helps businesses in understanding potential gaps as well as opportunities which result in enhanced profits for the business. It also helps in visualising problems and taking the right steps to solve the problem. This results in enhanced operational efficiency for the business which again helps the business in sustaining over time.

Setting Up the Process

When a business wishes to set up a process for data analytics it has to take one step at a time. There are multiple steps involved in the process. They are as follows.

1. Building the Right Team : In a startup its a team which determines whether its a success or a failure. Hiring people who share the same vision as the top management is extremely crucial. Definition of What, Why and how are extremely crucial and the team members must use their skills to achieve the result which is in sync to the vision of the top management.

2. Setting Up a Data Lake: Depending on the cloud system the business wants to operate on, they can select the data lake as per their preference. For example, if they are on AWS, they can select Redshift or Athena, for Google Cloud they can select Big Query, for Azure, they can select Azure Synapse and so on.

3. Setting Up the Data Extraction Process : Data could be lying across systems. Ino order to make optimum utilization of the data, it needs to be extracted from the databases and needs to be loaded into the data lake. Now, this could be done in two ways, by writing manual scripts, which takes a lot of effort and time or by selecting an ETL tool like Sprinkle, which extracts data from 100+ data sources and loads it into your preferred cloud data lake with just a few clicks and without writing any code.

4. Transforming  the Data for Analysis: The data could be transformed before loading it into the data lake or it could be transformed after it is loaded into the data lake. The earlier one is the ETL architecture, which is a much more traditional way and the later one is the contemporary way and suits modern businesses.

5. Selecting the Exploratory Data Analytics Process: Once the data is cleaned and transformed, it is ready for analytics. Analytics could be of multiple types. For example, exploratory, predictive, prescriptive and so on. The first step is to build an exploratory data analytics process. Platforms like Sprinkle come in handy at this stage as, it not only performs exploratory data analytics and empowers business users for the same, it also performs the previously mentioned steps as well.

6. Setting Up the Predictive Modelling Process :  Predictive data analysis is a much advanced step in the data journey of a business. Predictive analytics as the name suggests helps predict different aspects of a business and is generally a matured step in the data analytics journey. Businesses can select tools and platforms which enable predictive models which are in sync with their business objectives.

How Sprinkle Helps Start ups in their data journey ?

Sprinkle has been working with many startups right from their inception stage and empowering their data journey all along the way. Some of these organizations have now turned into unicorns. Sprinkle platform which scales as your business scales has been the choice of many startups like Swiggy, Byjus, Tata Cliq among others.

Sprinkle connects to all major data lakes like Redshift, Athena, Azure Synapse, Snowflake, Google Big Query and moves data from 100+ data sources in just a few clicks. All of this is done at a fraction of the cost of some of the other players in the market.

Sprinkle is a no-code ETL Tool and analytics platform which empowers business users to run their own analysis. This helps businesses reduce the adhoc report requests by more than 80% and also helps them save on resource cost as business users are able to run their own analysis without depending on the tech team.

Sprinkle is a complete data platform enabling businesses to run complete business analytics at one platform. Right from ingesting data from 100+ databases to building the reports and graphs on top of the data, all of this is done at one platform. This helps businesses in saving a lot of costs by investing in one platform rather than investing on multiple platforms.