Vital capabilities for advanced analytics success


Last month I looked at what organisations can do to overcome some of the barriers to tailoring data management for advanced analytics. This built on from the first blog on the advanced analytics topic, where I discussed how important data management is for successful advanced analytics. In the final piece of this series based on a recent TDWI report, I will explore the critical data management capabilities required to ensure advanced analytics success despite the challenging market conditions.

According to the results of the TDWI survey, the three most important data management capabilities are data integration and data warehouse (tied for first), and data quality. Of course, this should not come as a surprise considering that these form the core of what many consider to be modern data management from both reporting and analytics perspectives. Fundamentally, all three of these work in unison where it becomes difficult to distinguish between where the integration stops, and the warehouse begins.

A modern approach

Driving advanced analytics in this environment requires the modernisation of existing data warehouses. These need to be able to support the likes of data lakes that can be deployed across any environment whether on-premise, in the cloud, or a hybrid of both. But if the quality of the data is poor, it does not matter how integrated the data warehouse is or whether it supports modern innovations. Organisations will still not be able to effectively analyse the information they have at their disposal.

For its part, data integration extends beyond the warehouse to encompass aspects such as ETL, replication, synchronisation, virtualisation, orchestration, and workflow management. Furthermore, the report highlights the importance of data semantics, a broad term that incorporates all forms of metadata management. Without this semantics in place, self-service analytics cannot be successful. And if this does not take place, true advanced analytical programmes are also significantly limited in their potential for the organisation.

After all, self-service analytics include data tools and the preparation of data for more simplified integration.

Remember the interface

According to TDWI, data management for advanced analytics should integrate data from numerous sources at multiple latencies. And as the type of resources and targets are expanding via new interfaces, companies need to keep in mind the importance of effectively interfacing with these environments.

To this end, interface and API management are becoming increasingly important aspects of data integration and advanced analytics. As with other technological processes, the human element should not be overlooked.

Human factor

People-driven data practices are also important for successful analytics initiatives. By supporting data sharing functions, stewardship, and curation features, business users can more effectively manage and control analytics in the organisation and extract insights from the data. Data is changing how businesses operate and engage with external and internal stakeholders. By embracing the variety of data management capabilities available, decision-makers can help ensure the success of advanced analytics irrespective of what is happening in the market.

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