In today’s rapidly evolving and competitive business landscape, the potential for digital transformation through data has never been more promising. Organisations of all sizes are presented with an opportunity to support business growth using modern data solutions – encompassing databases, advanced analytics, and AI/ML capabilities. However, the journey towards maximising these benefits requires a well-thought out approach, where business objectives are aligned with the right technology choices.
Our experience has shown that embarking on technology-led data projects without a clear understanding of business use cases often leads to project failures. This results in not only wasted effort, but also has a significant impact on overall business confidence. To harness the true power of modern data solutions, it’s imperative to start with well-defined business goals based on real needs, and work backwards to develop the appropriate technological foundations.
Building your data platform
You need to decide on a data pipeline or integration technology. A data pipeline takes data from existing systems of record, combines or enriches this data from other data sources, and makes the data available to your end user systems via API or as an input into machine learning operations (MLops) which might end up being exposed to your users via a machine learning model.
Two popular choices for data integrations are AWS Glue or Databricks. Both can crawl, catalogue and process your data sources in a documented and repeatable manner.
At a high level, a typical data platform can look something like the this:
Not all data platforms are built equally. They will use a varying number of services based on the business need, but the above diagram shows some of the typical services that can be used.
Depending on your needs, you might need one take into account considerations when building out your data pipeline such as:
- Do you understand your data models? Have they been documented and agreed with your business stakeholders?
- Data integrations can be implemented as a batch process or streaming. Which to choose depends on your needs and each has different considerations:
- Batch processing is suitable when time is not of the essence. This is usually for larger datasets that take a while to process and real-time processing is not essential.
- Streaming data solutions such as Apache Kafka or Amazon Kinesis can be used to capture and near real-time events. This streaming data can also be queried using windowing within a specific time period to aggregate insights quickly.
- Connectors that extract data from existing data sources, both in your data centre or in the cloud, such as relational databases, logs or other third party data for later processing. It might be as simple as a built in AWS Glue connector. Or, if you are using Kafka, Kafka Connect has a multitude of options for connecting to external data sources.
- Will the processed data be made available to consumers via dashboards or reports by using a tool like Amazon Quicksight or will there be further downstream processing possibly via Amazon SageMaker for machine learning or backend API’s?
- What are your query patterns? Do you understand how your business will use the data? Do your data models support these query patterns?
Benefits of modern data solutions
Increased operational efficiency
Data analytics can act to expose hidden inefficiencies and wastage within business operations. Through careful analysis, organisations can identify bottlenecks, streamline processes, accelerate delivery timelines, and resolve issues promptly. This results in enhanced operational efficiency, reduced costs, and optimised resource allocation.
Insights for informed decision-making
Well-designed data lakes allow organisations to seamlessly access, analyse, and visualise data. As a result of this analysis, businesses can improve customer retention, boost product adoption rates, and ultimately tailor their offerings to suit market demands.
Personalised customer experience
Access to comprehensive customer data offers a holistic view that is key to helping deliver personalised services. By understanding individual preferences and behaviour patterns, organisations can tailor their offerings, creating unique experiences that resonate with customers.
Integrating contact centres and AI services opens doors to analyse customer interactions and thus provide improved customer service. With streamlined communication channels and AI-driven personalisation, businesses can elevate customer satisfaction, capitalise on upselling opportunities, and foster long-term loyalty and also ensures the organisation’s competitive edge.
Harnessing the future: Embracing modern data solutions
In a world where data is so critical for getting an edge ahead of the competition, organisations that strategically leverage modern data solutions can gain significant business outcomes. Choosing a technology partner who can help you gain a clear understanding of your business needs and help you build a solution with your business outcomes in mind is more important than ever.
At Cevo, we have the people, assets and methodologies that can work with your teams to unlock your data to further your business outcomes. If you’re interested in understanding the opportunities that modern data solutions can have for your business, get in touch to see how we can help.
