Data-Driven Companies

For any start-up, at some point, the big issue is the lack of data. 

To get data, the majority of companies try to reach out to potential customers and do market surveys, and perform competitor analysis to add more value to their data. However, that might be not so well-defined or structured approach, initially and can be time-consuming as well.

Web scraping, API, and free data sources can also be good alternatives in such cases as they do not involve much effort. However, that requires defining the objective and most importantly assessing the data quality. Analysis can be super good or super biased based on what you feed to the tool. Start-ups should probably take expert help to establish this process and create a seamless and scalable data pipeline. 

Once you have good Data, you can start learning to understand it.

Different companies have different KPIs and objectives, so the learning has to be specific to get the maximum outcome of the analysis. Just starting random analysis for the sake of doing it could be less productive. A good starting point, however, can be finding patterns, creating an analytical mindset, and finally, trying to connect the dots.

Data visualization plays a crucial role. It can give you insights, and help you reach quick conclusions at initial levels. Creating charts, graphs, and dashboards would help you identify trends, filter out noise and finally, get a full-fledged overview.

Research finds analytical CEOs are super analytical in their personal life as well.

Smart Analytics does not have a unique definition.

It can be anything from building a simple dashboard to creating a cloud-based real-time analytics stream. It should help you in making decisions. 

Data has a role. As you analyze it, it helps your organization react to it. The better your analytics system's health, the better would be organization's reaction to the market conditions. For example, stock market companies need not only 24*7 streamlined analytics but also a good financial modeling plan.

Building smart analytics would help you measure your performance and improve your customer service. Ex- Uber's research team invested a lot in its smart analytics to reduce customer waiting periods. 

It really depends on the number of KPIs, the nature of the data, and the features of the platform. 

A good analytics system should have at least 2 KPIs that the business would like to track on a periodic basis, 2 KPIs that would focus on Customers, and 1 KPI for operational efficiency.

A real-time analytics system would be a better option but might be costly as well. It would not only need investment in cloud architecture but also a well-designed data pipeline so that data ingestion has a high throughput. Most daily/monthly batch jobs that run for operational purposes can be non-real-time analytics systems. But, if you are running financial transactions ( can be ATM -POS or stock market analysis), you must need real-time analytics. 

Non-realtime can compromise with speed but real-time can not. Therefore, the effort of building real-time analytics would be considered high compared to other options.

There are no limits to adding features to your system. However, all smart analytics systems should have data ingestion, streamlined processes, and analytics & reporting. The common problems are data corruption and it's very difficult to modify corrupted data. Many start-ups fail to design a good data ingestion feature and that corrupts the data format and signifies the issue further. Sometimes, it might need manual assistance and it can make it even worse. So, designing analytics features can be very challenging and requires most of the effort.

Small companies generally start with non-real-time analytics systems and thereafter, migrate to more sophisticated applications based on requirements. The timeline can be anywhere between 2 - 4 weeks with proper testing and a minimum feature set.