Data Science Without Big Data? Here is how to get best insights from limited data.

You don't need a data warehouse to run your critical analytics initiatives. If you want insight with limited data, consider these four priorities.

Data Science Without Big Data? Here is how to get best insights from limited data.

CIOs too often put off data science initiatives until they can build a strong data engineering layer. They wait until a data warehouse is available before planning a data analytics project and believe that advanced analytics is essential to transforming business, and that large volumes of neatly organized data are a prerequisite for this. There is nothing more in the truth.

There is no big data, but there are 4 things to keep in mind if you are looking to undertake your data science initiatives. One.

1. The business problem should determine the type of analysis required.

Gartner estimates that about 80% of data science projects fail to deliver business results. The main reason  is that leaders are not choosing the right business problem to solve. Most data analysis projects are selected based on available data, available technologies, or available toolboxes. This is a recipe for failure. A data analysis project should not start with  data or analytics. The best way to start  data science  is to analyze your organization's strategy. By finding and solving the most important problems your target audience wants to solve, you can achieve the desired business impact. The  business objectives you choose will determine the analytical approach you need to take and  the data you need.

Lack of data in the first place can also be an advantage. Start clean and you won't be burdened with old luggage. On the other hand, organizations that have existed for much longer periods often struggle to undertake costly digital transformations.

Think of Moderna, which has been creating a digital-first culture since its founding in 2010. The company has created a data and analytics platform to address mRNA drug development business priorities. This targeted approach allowed Moderna to create a COVID19 vaccine project in just two days.

2. The analytical approach determines the data source.

Organizations can spend months building a data warehouse to discover that the data they collect isn't enough to perform the necessary analysis. Machine learning algorithms often require data of a specific type, volume, or granularity. It is a waste of effort to try to create an ideal level of data engineering when it is not clear how it will be used.

Once you understand the organizational strategy and  business problems you need to address, the next step is to perfect your approach to analytics. Find out whether you need technical, diagnostic or predictive analytics and how your information is being used. This will make it clear what data you need to collect. If data retrieval is an issue, we phasing out the data  collection process to ensure iterative progress of the analytics solution.

For example, executives at a major computer manufacturer we worked with wanted to understand what drives customer satisfaction, so they created a customer experience analytics program that started with direct customer feedback through voice customer surveys. The descriptive insights provided by the story helped boost the promoter's net score in the following survey:
Over the next few quarters, we expanded our analysis to include social media feedback and competitor metrics from sources such as Twitter, discussion forums, and double-blind market surveys. They used advanced machine learning techniques to analyze this data. The solution has helped generate an additional $50 million in  revenue for customers annually.

3. Data collection begins with small, readily available data.

Big data often comes to mind when you think about the premise of machine learning. However, it is a misconception that business transformation requires large amounts of data. Many executives mistakenly believe that it is necessary to  collect millions of data points to find hidden business ideas. After you have focused on your goals, your business goals, and your analytical approach, the next step is to collect your data for analysis. Many business problems can be solved with simple descriptive analysis of small data tables. By reducing the data entry threshold to hundreds of lines, you can digitize paper records or manually collect data from the system by setting up a simple system to collect the required data.

Many executives mistakenly believe that it is necessary to collect millions of data points to find hidden business ideas. In another example, a mattress manufacturer we worked with wanted to use analytics to improve productivity. As a midsize company early in its data journey, the company had a small amount of data, mostly composed of hand-prepared tables. Instead of deferring analytical retrieval, the company embarked on a diagnostic analytics project to optimize profitability.

He digitized machine data on paper, combined it with manually prepared data from several spreadsheets, and then used simple statistical techniques to analyze hundreds of rows and determine which ones to use for optimization. By discovering ideas such as optimizing production batches to adjust for temperature and humidity, the recommendations identified a potential 2.3% yield increase, resulting in an additional $400,000 increase in  annual revenue.

4. Adopt a step-by-step approach to achieving transformational data value.

The key  here is not to stop data science initiatives due to the limited amount of data. This is by no means a sequential process. Use a design mindset to identify the right business problem to solve. Use agile to develop the right analytical approach to problem solving. Finally, develop an iterative process to get the data you need step by step.

In most scenarios, it is impossible to think ahead of all potential data sources needed. If you are just beginning your data science journey, evaluating analytics best practices is a waste of resources. This leads to a paralysis of redundant engineering and analysis. Remember that just doing a data analysis project can help you build a solid data engineering roadmap.

This iterative process will result in more efficient, effective, and innovative value. Get the momentum by delivering results quickly with a byte data analytics solution. By focusing on user decisions, you can turn insights into business decisions and ultimately get a return on your investment.