Managing the Difficulties of Starting Data Analytics Capabilities for Small to Medium Companies?

Every company wants to get on the Data Analytics gravy train. Every company wants to have its data analytics team. Fear of Analytics Missing Out, FOAMO, plays a considerable part in driving this frenzy. Big companies spend a ton on building data teams. This hiring frenzy drove up the demand and salary for data personnel, and leaving SME is a challenging position.

Small and medium-sized companies (SMEs) face a variety of challenges when it comes to building analytics capabilities. Not so surprised that the return on investment for these small and medium companies is lower than the management had anticipated.

For the last ten years, I collaborated with several small and medium companies in Thailand, helping them build data analytic capabilities. In this article, I like to share my experience in managing the difficulties of reaping the financial benefits of building a data analytics team of small and medium companies.

Here are seven reasons why it can be difficult, along with some suggestions for overcoming these challenges:

1. Limited resources: SMEs often need more financial and human resources, making investing capabilities difficult. To overcome this challenge, SMEs should consider outsourcing analytics tasks or partnering with other companies to share resources. However, choosing who to outsource must be carefully considered to avoid snake-oil vendors.
2. Lack of expertise: Many SMEs need in-house expertise to build and manage analytics capabilities. One way to overcome this challenge is to invest in employee training and development programs or hire external consultants with the necessary expertise. But first, spend much time focusing on what skills are needed.
3. Data quality issues: SMEs may need help with incomplete or inconsistent data. To overcome this challenge, SMEs can invest in data cleaning and validation processes or use data quality tools and software. In the beginning, a quick solution is outsourcing data cleaning. However, most SMEs do not spend adequate resources on data quality.
4. Limited data sources: SMEs may need more data sources available, which can make it difficult to build comprehensive analytics capabilities. To overcome this challenge, SMEs can consider partnering with other companies to access additional data sources or explore new data collection methods. Presently, several vendors in Thailand are offering data sources as a service.
5. Difficulty integrating data: SMEs may need help to integrate data from multiple sources, such as customer data, sales data, and marketing data. To overcome this challenge, SMEs can invest in data integration tools and software or work with external partners who can help with integration. Typically, SMEs only have a few data sources, so this is not a big problem. However, it is worth planning for data integration now instead of later.
6. Lack of strategic alignment: SMEs may need help to align their analytics efforts with their overall business strategy, which can make it difficult to generate value from analytics. To overcome this challenge, SMEs can work to develop a clear analytics strategy that aligns with their business goals and ensure that analytics efforts are integrated into key business processes. Unfortunately, most SMEs in Thailand do not spend enough time planning and aligning their data and business strategies.
7. Resistance to change: SMEs may face opposition from employees not accustomed to using analytics to inform decision-making. To overcome this challenge, SMEs can invest in change management processes, such as training and communication programs, to help employees adapt to new working methods. This is not a big problem in most employees embrace analytics.

By understanding these challenges and taking steps to address them, SMEs can build analytics capabilities that help them make data-driven decisions, improve their business performance, and stay competitive in today’s fast-paced business environment.