Increases in data continues to put pressure on organisations to modernise their data architectures so users can make better, more informed decisions. Data architectures that depend on legacy, one-size-fits-all BI and data warehousing systems and approaches are often falling short as use cases now push beyond simple, predefined data consumption and involve more ad hoc interaction. Here we’ll look at five key concepts to modernise your data architecture that can help to drive more effective data analytics and better business decisions.
1. The Unexpected Highlights The Importance Of Data Agility
As we’ve seen business conditions change so real-time and predictive insights are critical to adapt to unexpected events. COVID-19 related disruptions continue to impact supply chains, logistics, the workforce, customer demand, security and general sentiment about the economy. Users need current, complete and accurate data so they can ask new questions using ad hoc queries and investigate the full range of new and historical data sources. Taking advantage of real-time data for analytics involves special considerations from a data architecture perspective. Implementing a modern data and analytics platform allows us to gather, store, and process data of all types and sizes from any data source. Unifying the data lake, data warehouse, and other data silos in the cloud would benefit implementation here. With real-time data from logs, devices, and sensors streaming into data systems, organisations need modern tools and data platforms to support powerful operational analytics that answer complex business questions.
2. Get Your Head In The Cloud
An enterprise data warehouse brings all your data together, no matter the source, format or scale, for a single source of the truth. It also provides a way to run high-performance analytics on your business data, to gain insights through dashboards, operational reports and advanced analytics. With the advent of cloud computing, traditional data warehouses and data strategies have been transformed. Cloud computing can offer scalability, flexibility, cost elasticity, and fast deployment. More than half (54%) of organisations surveyed by TDWI in its best practice report found they already have traditional data applications such as enterprise BI, reporting, and dashboards running in the cloud, and nearly as many (51%) have business-driven self-service BI and analytics. In 2023 companies should focus on building out strong cloud data foundations that allow them to take advantage of the most important benefits the cloud provides such as scaling applications or automatically adding capacity to meet surges in demand.
A complete data strategy should cover the entire data life cycle from ingestion to storage, use, sharing, and ultimate archiving or destruction.
Data strategies must balance business-driven flexibility and self-service with the benefits of enterprise governance, coordination and integration. However, the preeminent data strategy goal should be to streamline the path to unlocking business value. To learn more about why Microsoft Azure Cloud Services is worth a look read our earlier article here.
3. Make Data Accessible
As businesses modernise their data architectures, they should not overlook problems in accessing data held in legacy applications and systems. Some mainframe systems hold valuable data in older relational or nonrelational databases, flat files, and storage devices that play a vital role in daily operations. Many organisations today have a long-term strategy of migrating ERP, CRM, and other business applications to the cloud. However, the size and complexity of these applications make full cloud migration slow and difficult. To provide complete access to data sooner, businesses can look at replicating data from legacy systems into the cloud or using ELT routines to load data directly into cloud data stores like a cloud data lake as the target platform and then perform transformations after loading. This approach has the advantage of being able to use powerful and scalable cloud-based MPP database engines for faster and more workload-specific data transformation and cleansing. Depending on business requirements, organisations could decide to focus replication and extraction first on active transactional and master data, which might be the most valuable to users, and then follow with replication and migration of older historical data.
4. Establish A Unified Metadata Repository To Find Trusted Data Easier
The more the data, the more the need for accurate, well-managed metadata. A complete and up-to-date repository of metadata, such as in a data catalog, makes it easier to establish consistent data and table definitions and discover schemas, mappings, transformations, and other critical information about data. A centralised metadata repository helps businesses reduce confusion about the data’s quality and consistency and makes it easier for users to search and find the data they are looking for.
5. The Power Of Self-Serve Analytics
Self-service analytics solutions integrated with a cloud data platform enables business users to respond to immediate, even unexpected, needs for data visualisation, analytics, predictive model development, and new data exploration. Ease of use is a critical success factor for these types of solutions. It also doesn’t mean users need to start with a blank slate, businesses can predefine dashboards and reports with user input that are most common, but in a way where parameters can easily be personalised by the user. It’s important though that:
- The full range of data is available to enable true adhoc analysis allowing for a range of use cases
- Self-service BI should be integrated with data catalogs to understand quality and data lineage
- Investment should be made to improve data literacy across the organisation to increase user proficiency, while also educating on how using data has responsibilities associated with it
Modernising a data estate isn’t always easy; it involves introducing fresh processes, using new tools, and champions to see cultural changes through. At BoomData, we’ve assisted a number of organisations modernise their data and analytics platforms, this means consolidating, storing and integrating all of your business data into a single repository and then presenting it for better analytics and reporting. There’s no need to wait for complete cloud migration of complex business applications and systems to start reaping the benefits, organisations can use the cloud to make critical data available sooner so users have complete access and are not blind to unexpected situations, trends, and patterns.
