Data Processing and Integration – The Smart Way

As organisations gather more data, storage and processing become key concerns. But, by turning to enterprise data warehouses, they can manage multiple data sources, keep all data sources centralised, and simplify the task of ensuring good data governance. If established in the cloud, rather than on-premises, they can also use cloud-based processes to deliver critical business intelligence.

Processing data in the cloud

Enterprise data warehouses support a wide range of processes that are central to making smarter business decisions. These include:

• data mining to identify trends;
• extracting data to answer specific queries;
• visualizing data sets;
• sharing data with stakeholders.

Shifting data to the cloud rather than keeping it on premises makes it available to a wider range of stakeholders, who can self-serve, subject to authorisation, wherever they happen to be. In the age of the hybrid office, this has never been more important.

Real-time analytics and the total cost of ownership

Moving storage and processing to the cloud gives organizations access to more flexible infrastructure, allowing resources to be spun up and decommissioned based on demand. It’s a more cost-effective way of working, which allows for better budgeting and near-instant implementation. In turn, organisations that adopt this model can expect to extract value from their data more quickly.

Outsourcing data need to specialist providers

Moreover, by outsourcing to specialist providers, they frequently benefit from ongoing upgrades, giving them automatic, built-in enhancements over time, and accelerated machine learning for more effective artificial intelligence.

When organisations work with alternative data rather than their own metrics, outsourcing makes even greater sense. Service providers, like Merit Data and Technology, which gathers more than four million data points daily from more than 100,000 online sources, have access to a far greater diversity of resources, which can inform richer machine learning for more insightful AI.

Outsourcing analytics of this type to a specialist provider frees client organisations from developing equivalent expertise in-house, at cost, or from mining the required data. Between 2020 and 2026, the market for outsourced data analytics is expected to experience compound annual growth rates of 21.5%. This would suggest that organisations increasingly recognise the importance of timely insights in stealing a lead on their competitors.

The benefits of third-party software and their product life cycles

Where organizations continue to perform their own analysis – alongside outsourcing – platforms like Tableau, Oracle Analytics Cloud and Microsoft Power BI simplify the process of analysing data in an enterprise data warehouse. Accessible user interfaces, expose many of the tools’ functions via an easier-to-use platform that doesn’t require knowledge of programmatic tools like R or Python.

Constant innovation within each of these platforms means users will benefit from changes made in response to observation of, and interaction with, the broader user base by platform developers. This is a key benefit often not available to organisations deploying a bespoke system, in which they’ll need to not only develop innovations themselves, but ideate them in the first place.

Database instances and database design

A data warehouse can accommodate many databases for a single client, and service multiple clients simultaneously. While the data in each instance will be kept separate, clients can combine several sets to deliver a query response.

Several different architectures can be used to manage the data and databases within the warehouse, including:

• a simple architecture with raw data, summaries and metadata stored separately but alongside one another for regular querying, optionally with an additional processing area for data that has yet to be cleaned and processed;
• a hub and spoke architecture, with a central master data store supplemented by derived sub-repositories for use by specific processes, projects or departments;
• a sandboxed architecture, with multiple datasets that don’t interact, or multiple copies of a single data set for safe experimentation.

In each instance, it is essential that the data in the database is accurately described and labelled using metadata, which itself is contained within a metadata store.

Generating metadata

The data stored in a warehouse will grow over time – and needs to be properly indexed if it is to be of greatest value to the data owner. Accurate metadata is essential and is used through the life of the data it describes, including when querying metrics, cleaning the database and extracting information. Different types of metadata include:

• Technical metadata, which describes how the data to which it relates is indexed and defines the format of each field so that only valid data is accepted;
• Operational metadata, which describes where the data came from, how it was acquired, and how it was transformed during import;
• Business metadata, which describes who owns each data point, and how it is to be used;
• Descriptive metadata which, as its name suggests, describes the content itself. This can be hand-crafted, but computers are getting better at identifying contents and contexts themselves, allowing for automatic indexing of subjects in photos and documents.

In a Graph database, the relationship between discrete data points is also relevant, and can itself be considered a form of metadata, describing the relationship between a subject and an object within the database.