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Creating Value From Data With Automation

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Creating Value From Data With Automation

In this article, we discuss the importance of data warehousing in order to better create value from data with automation.

· Big Data Zone ·
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According to the 2019 Gartner fourth annual Chief Data Officer (CDO) Survey, the implementation of a data and analytics strategy was ranked as the third most-critical success factor when it comes to a CDO's organization.

When it comes to data, we’re all aware of the four "Vs" — variety, velocity, veracity, and volume – yet for many organizations, their data warehousing infrastructure is no longer equipped to handle them. Additionally, value, the fifth "V," is even more elusive. So, taking into account the scale of data that many modern companies have means that meeting these challenges requires a new approach — with automation being the foundation.

45 percent of a CDO's time is spent looking at methods of using data for value creation and revenue generation. This means being able to harness data in a way that is realistic, practical, and actually beneficial. A data warehouse can help meet these expectations, providing enterprise data with a centralized space that business users, the CDO included, can use to develop insights.

For a CDO to succeed in monetizing data within an organization, creating a successful data warehouse is crucial.

The traditional waterfall approach to data warehousing that was first introduced in the 1970s, however, only delivers a mere fraction of the value it potentially has to offer.

This approach needs to evolve to address new data sources and adapt to business demands – essentially becoming more responsive as organizational needs change. Using automation software to design, develop, deploy, and operate data warehouses provides a wide-ranging value to business leaders. This change gives flexibility when business needs demand it and incorporates new data sources and technologies more easily.

You may also like: Best Practices for Collecting, Storing, and Delivering Decision-Support Data.

What Can the CDO Do?

Data warehouses are invaluable for providing business users with the information they need, as they are the central storage point for enterprise data. Yet, the gap between user expectations and a data warehouse’s ability to provide up-to-date, consumable data in a timely manner has grown, motivated by users becoming more aware of the potential benefits of data-driven decision making.

Businesses both want and need insights from data faster than ever before. Additionally, the ever-rising growth of new forms of data intensifies this business need, particularly when it comes to semi- or unstructured information, such as client communications, real-time messages, sensor data, social media, and audio/video files.

Customarily, data warehouse development and evolution meant long-cycle IT projects, which contrasted heavily with the needs of more agile project design and build environments. To support digital transformation efforts, CDOs should take the lead in rearchitecting data warehouses, from creativity to acceleration and automation in order to increase a business’ time-to-value ratio.

Introducing Automation

These days processes need to change, particularly as IT departments are expected to do much more with a lot less.

Rather than spending time crafting a bespoke data warehouse infrastructure with unique configurations and a longer lifespan, IT teams should be focusing on producing a flexible decision support infrastructure. Essentially, creating a data warehouse that can easily transform along with business needs.

“The traditional waterfall approach to data warehousing that was first introduced in the 1970s only delivers a fraction of the value it potentially has to offer ”

CDOs can help their organization achieve this by following these five steps:

1. Know what the desired outcome should be

CDOs need to understand the specific challenges business teams face that data could help out with, before making any type of decision as to the future of the data warehouse infrastructure. Fundamentally, a data warehouse automation and modernization program needs to be built around assisting decision-making, leading to differentiation in the market place.

A recent TDWI survey suggested that the top reason for data warehouse modernisation is the realignment to business objects. The CDO can help chart the course for how business goals and technology meet by enabling collaboration between business and IT teams. This will , in turn, lead to overall business transformation, enhanced by the new data warehouse’s approach to data-driven decisions.

2. Know what you already have

Sophisticated data management tools are already deployed as part of most organizations’ data infrastructure. However, these may not be working to the best of their abilities. Companies that are already using Oracle, SQL Server or Teradata have a range of data management and movement tools already within their IT real estate. These can be automated and leveraged more effectively as part of a data warehouse automation push.

Nevertheless, throughout the inventorying process, CDOs should be ensuring they have thought about the capacity requirements of their data warehouse. It’s no secret that data is continually growing at an exponential pace, so even if the data warehouse is fit for purpose today, it’s important to ensure the automation processes, general infrastructure and storage requirements are of a speed and standard that is capable of handling this in the future too.

Additionally, it’s important that data warehouse automation integrates with the business, as it currently is, and will authentically be in the future, rather than as the business believes it may be in an ideal world. CDOs need to encourage their teams to understand the value of the data available, along with the automated analytics and evaluation processes that can be used to meet precise business priorities.

In order to support this, it’s essential to design the data warehouse automation strategy for not just an ideal set up of data, but for the realistic unpredictability of the business data landscape. Data modeling approaches, such as Data Vault 2.0 can be automated, to provide even more flexibility to organizations to easily address change.

3. Ensure automation is efficient

As with any other large-scale transformation project, data warehouse automation requires resources. However, these are often scarce due to strict budgets and competing priorities. So, CDOs need to think about what should actually be automated in order to free up future man-hours. Hand-coding SQL, writing scripts, or manually managing metadata are all examples of how automating tasks can be more cost-effective. All of these systematic processes can either eliminate the need for human involvement (and, indeed, human error) or dramatically speed up the process.

4. Always be open to change

Data warehouse automation and modernization should be seen as an avenue of constant, on-going development. CDOs should be able to re-strategize different parts of the business’ infrastructure to match business needs and any new data sources that may emerge.

CDOs should also look to take a staged approach to the initial automation and modernization process to minimize disruption and ease the transition for business users, setting out a schedule of when each different requirement should be met. Additionally, due to ever-changing business needs and new technologies being used, post-production change is inevitable and has to be planned for.

CDOs should also make sure to prepare for the human change that automation will create. In business teams, users can be redeployed to increase efforts on analyzing business intelligence, and in turn, converge the insights into business value. Elsewhere, in IT teams, automation gives a new scope to plan for the future by looking at new analytics tools or planning for better and smarter ways to deliver on business priorities in the future.

Enabling a Data Warehouse Automation Mindset

Data warehouse automation is more than just software you purchase; it’s also a culture you implement into a business. Yes, tools and technologies form the basis of the process, but a good data warehouse strategy needs a transparent process, strong leadership, and an unwavering focus on the business’ end-goals in order to thrive.

Businesses will find it hard to fully leverage the potential of data and its related technologies without robust data automation. It’s important that CDOs take responsibility when it comes to data-driven transformations, seeking out the best ways in which large-scale data usage can guide future business decisions and ensuring that the fifth "V" — value — is always taken into consideration.


Further Reading

Topics:
data ,automation ,big data ,python ,cdo ,r ,statistics ,machine learning ,tutorial

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