Mid-market enterprises stand at a crucial point as technology in the hands of the right employees continues to be a great equaliser. We’re finding that many mid-market businesses are striving to strengthen their data and analytics foundation and capabilities while also looking to explore cutting-edge technologies such as generative AI. However, there’s work to be done, the very nature of these businesses dictates a different approach to data and analytics for mid-market firms is required.
Mid-sized firms in Australia, let’s say (typically $30 million turnover to about $750 million turnover) are stepping up their data and analytics game to stay competitive, be compliant and grow cost effectively. In fact, 94% are fast tracking plans to modernise legacy systems and 85% are implementing new data platforms and governance standards to do just this, according to Avanade’s November 2024 AI Value Report. However, there’s work to be done – poor data quality and governance are inhibiting progress, 1/3 have too few resources to deploy data and analytics effectively and more than half of these don’t have trust in what the data is telling them (SAP mid-market firms research October 2024). So given this, here are some thoughts to improve capabilities in the area of data and analytics for mid-market firms:
1. Recognise That Your Different
While mid-sized firms can have similar complexity to larger companies, inherently there are some unique differences. There’s usually fewer specialist inhouse data and analytics resources, can be minimal data and analytics leadership and lower data literacy in general across the business. Business users need to be enabled with simple-to-use analytics and dashboards, cost and efficiency are key factors and agility and speed are paramount. All these characteristics dictate that data and analytics programs should be lean, scalable and business-driven with automation, actionable insights and user empowerment at its core.
2. Do You Really Have A Data Strategy?
First and foremost, understand how important the use of data and insights is to your business. Do you have a data strategy and a clear digital vision with a roadmap? How well would you rate yourself to self-assess your data and analytics capabilities and readiness? Have you done any benchmark data and analytics assessments? Partners like BoomData can help with this.
3. Use The Cloud To Improve Data Integration
Unlike large enterprises with fully integrated data ecosystems, mid-sized firms often deal with disconnected systems (ERP, CRM, supply chain tools, etc.). This necessitates lightweight, flexible integration strategies – such as cloud connectors, APIs, and no-code ETL solutions – to bring data together.
4. Balance Customisation & Scalability
Many enterprise data and analytics solutions can be overkill for mid-market firms, while small business tools may not be powerful enough. A tailored approach using modular, scalable data platforms like Snowflake and Microsoft Azure allows mid-sized firms to start small to better integrate, organise and manage data for reporting while leveraging the benefits of the cloud to grow capabilities over time.
5. Prioritise Agility & Speed
Mid-market businesses must be more agile than large enterprises to compete. It’s not feasible to have long, complex data projects. Instead, rapid insights that enable quick decision-making – favouring self-service analytics and real-time dashboards like Power BI over heavy, IT-driven implementations. With fewer dedicated inhouse data professionals, business users must be able to work with data. Tools with intuitive interfaces, AI-driven insights and automation help non-technical teams leverage analytics without deep expertise.
6. Investing In Data Governance Is Important
Data governance is a balancing act between stability and agility. To understand the level of governance required consider the role that regulation, compliance and legislation play in your business, the industry you’re in and your competitive advantage. Businesses should define what they consider to be data, document this and standard operating procedures over your data. Look to implement a data catalogue with tools like Microsoft’s Purview, that will act as a map for other data governance initiatives and can help automate scanning of metadata. While creating a data glossary will help users easily find what they’re looking for. Understand what attribution you want to capture and link these (examples would include columns, tables, APIs, people, systems, business terms, data products, lineage.) Linking all of this together, along with additional advanced concepts such as data lineage and data quality will provide a strong foundation to ensure reliable, secure and trustworthy data. Here we explore further how to build trust in your data.
Where To Next?
As specialists in data and analytics for mid-market businesses, BoomData has a proven data and analytics framework that is lean, scalable and business-driven. We offer advisory, scoping, design, delivery and support services in the areas of cloud data platforms, data engineering & integration, data governance, business intelligence, reporting and analytics. We’re here to help by knowing our stuff, guiding you on the journey, delivering at speed and by being easy to work with. If you’re looking for assistance reach out, we’d love to hear from you.
