Why Only 30% Of Data Science Projects Succeed?

A lot of enterprises talk about data. There are several successful examples and use cases of data-driven strategies. And yet, many are still unable to exploit their collected data.

A PwC surveyof more than 1800 companies – including mid-size companies with more than 250 employees, as well as enterprise-level organizations with more than 2500 employees – found that the companies have a lot of progress to make with the data before they start making better use of the data. And only a small percentage of companies reported effective data management practices.

The Director of PwC Risk and assurance, Richard Petley, stated that “Data is the lifeblood of the digital economy. It can give insight, inform decisions and deepen relationships.” However, he also described his concern over the sensitiveness of data, as it can be stolen, can be bought and sold to the third party. While organizations are attributing more value and concern on data, yet they do not have the capabilities to manage, protect, and extract that value.

Data Challenges Faced by Companies

Research has found that more than 70% of the data projects fail. Clearly, companies are facing challenges with respect to management, collection, access, and segregation of their data. The top challenges include –

  • Lack of data science talent
  • Lack of financial support
  • Difficulty in accessing data
  • Explaining data science to others
  • Lack of domain knowledge expertise
  • Privacy issues
  • Expectations of project impact

7 Reasons why Data Science Projects Fail

It will be no surprise that maintaining the quality of data is the biggest challenge for the enterprises. Most of the data scientists spend considerable time preparing and cleaning up unnecessary data. The other aspects of data like privacy, relevancy, and access are also some critical issues. Let’s take a look at various reasons why companies fail to get the maximum impact from their data science investments –

  1. Lack of tools and techniques

Most companies lack a technical understanding of data gathering, machine learning algorithms, and their heterogeneity. These challenges increase when the data volumes increase. Most of the basic tools reach their limits very quickly and need to be optimized along with additional computing power. The similar problem happens with the machine learning algorithms – these algorithms also need to scale as the volume and variety of data increase. 

  1. Budgets

Insufficient budget allocations impact all the aspects of the project, including hiring the right talent for using the right tool. That apart, the size of the data has an impact on the costs. The management costs of more critical data are higher. Companies need more expensive technology stack, more expensive resources, more computing power, and more powerful algorithms. The only way to tackle the budget is to hire the right talent. They can contribute to every phase of the data science project – right from planning the strategies to hiring and managing the teams.   

  1. Unavailability of analytics translators

The role of the analytics translator is vital in a data science project as this person unlocks the value of the data. He/she helps the businesses identify high-impact analytics use cases and translates those to the data engineers, data scientists, and other tech experts. The analytics translators should also be involved in scaling the solution across the organization and generating business use cases. This skill set is unique, and thus, it is rarely available to the companies.

  1. Isolated analytics capabilities

While organizations try to embed data analytics capabilities into their business functions, many of them fail to create value as they tend to develop these capabilities in isolation. When the data analytics capabilities are decentralized, there is a considerable risk of all the data models not being connected. It also impacts decision-making when everyone across all the departments cannot access or analyze data for real-time decision-making. 

  1. No clarity on RoI

Companies end up joining the data bandwagon and invest millions of dollars on advanced analytics and digital tools without first defining their goals and objectives.  Unless they clearly define exactly what they are looking to achieve, it is hard to measure the RoI and know if the project was a success or a failure. All the stakeholders involved should first agree on the business use cases they want to address with data and define the KPIs to measure the results.

  1. Lack of data strategies

Studies reveal that 75% of business leaders feel they are making most of their information assets, but only 4% of companies are set up for success. Only 40% of companies have obtained some tangible benefits from their information, and 23% derived no profit at all. This means that three-quarters of the organizations lack the skill and technology to use their data to gain a competitive edge. The problem clearly lies in defining a clear data strategy. 

  1. Team challenges

Getting the right team in place is one of the greatest factors that can affect the success of data science projects. Any data science project revolves around three significant aspects – the business, the data, and the IT. Getting the right team comprising of expertise in all these areas and ensuring smooth communication and collaboration between them is critical. Lack of teamwork leads to lack of support, unimplemented results in the business, difficulty in explaining the data science, and complexity in project deployment in coordination with the IT.

Data science is no more just a fancy word dominating CXO conversations. It has arrived, and companies around the world are benefiting from it. To ensure the success of your data science initiative, clearly define the goals and objectives, set up the right team with all the stakeholders, empower them with easy-to-use and powerful tools and technologies, and most importantly, democratize data science. Allow everyone within the company to leverage data to make real-time, data-driven decisions.