A data-driven approach has become truly realised as essential in the highly competitive and fast-paced landscapes of today. Demonstrating essential agility alongside a wide range of use cases, Business Intelligence allows enterprises to maintain the competitive edge while gaining access to previously unavailable insights.

From understanding customer behaviour, to predicting the outcomes of various marketing strategies, enterprises around the world have realised the importance of basing strategies on intelligence rather than instinct.

As a response, organisations continue to evolve and change how they interact with data daily, streamlining data processes while emphasising consistency, reliability, and efficiency of results. Accelerated by the recent pandemic, organisations have also enabled more flexible access to data through adopting a cloud-based approach to data storage.

Evaluating evolving data concepts

As organisations continue to evolve and evaluate how they interact with their data – assessing how data continues to improve and affect the operations of organisations – we look at the future, examining how current concepts are beginning to transform, and how they will evolve to continue providing benefits and value in a rapidly shifting and transforming landscape, focusing on four core areas:

1. The need for accessible tools, and the potential loss in capabilities.
2. Ongoing transformations and limitations of Machine Learning.
3. The rapid shift to the Cloud, and the potential to eliminate on-premises storage altogether.
4. Master data management and awareness of critical practices.

The need for accessible tools, and the potential loss in capabilities

As the importance of data-driven approaches within enterprises continues to be realised and prioritised, tools such as Microsoft Power BI have focused on creating an experience that is accessible and promotes ease-of-use, ensuring that teams that do not possess current advanced data knowledge or technical skill will still be able to access essential insights gained from analytics practices.

As we look to the future, we must examine if this emphasis on accessibility will reduce the quality of insights in the process.

Currently, the challenge facing data workers is in how they can reach their core objectives through BI and analytics tools. As the objectives of enterprises become more complex – with Machine Learning and AI becoming more widely discussed, tools must adapt to provide access to more capable resources.

However, as the core objective of BI and analysis tools is to empower the user to reach results, rather than simply providing results themselves, the knowledge and experience of those interacting with data must evolve to ensure correct processes.

With the emergence of roles such as Data Scientists becoming more commonplace, enterprises are realising that accessible and easy to use BI software are only capable of delivering results that carry the same quality as the knowledge of the user. While these insights are important, a more sophisticated user is needed to reach optimised intelligence.

By focussing on the experience and skill of the data worker, rather than the ease of use of the tool, businesses may also encounter advantages previously unavailable – such as discovering key use cases to further streamline and optimise processes.

Learn more about Microsoft Power BI in our blog here: ‘Power BI: The Route to Flexible Business Intelligence.’

Exploring the ongoing transformations and limitations of Machine Learning

Machine Learning has quickly become one of the most discussed tools currently populating the Business Intelligence and Data Analytics landscape, promising the ability to streamline and automate repetitive practices.

However, as the capabilities and future of Artificial Intelligence within analytics continues to be explored, many are now considering the future use cases of Machine Learning, hoping to gain access to innovative tools and results before the competition.

Currently, Machine Learning tools can enable predictive analytics and apply automation to advance productivity and enhance strategies, as well as efficiently streamlining repetitive processes such as production manufacturing. Recently, many have begun questioning the ability to independently complete more complex processes.

This discussion isn’t purely limited to Machine Learning within Business Intelligence either, as scientists continue to explore the capabilities of Artificial Intelligence without human supervision.

Besides the current need for human supervision, another core limitation that must be considered before enterprises employ ML tools is in the ability to produce value. While deploying ML capabilities to enable a streamlined process is achievable, utilising ML to add value to a data strategy is a greater, and more critical task. For a more sophisticated and value-led approach, Machine Learning tools must incorporate information that may not be accessible, such as competitor insights and market value.

Learn more: How Machine Learning revolutionises your business insights

This need for inaccessible data, and the lack of capabilities for adopting a more independent approach, severely restricts the ability for ML processes to return a value that exceeds the complications of integration and implementation. However, as innovations continue to dominate this field, we look forward to seeing what the future of ML and AI within analytics holds.

Examining the rise of Cloud storage

Accelerated by the pandemic and the need for enterprises internationally to resort to working remotely, the advantages, benefits, and use cases of a Cloud-based approach allowed many to continue regular business operations during a period of heavy disruption and risk.

With advantages that far exceed an on-premises data storage solution, the Cloud allows for scalability, flexibility, and accessibility in a cost-effective platform. With more enterprises than ever before shifting to a completely cloud-based approach to data storage, it seems increasingly likely that we will see a complete abandonment of on-prem within businesses.

However, with the rise in adoption suggesting that the possibility of abandoning on-prem in all but the most necessary of locations, such as national security, one factor continues to hinder Cloud implementation and adoption: security.

Unable to view data storage solutions in a physical location, some enterprises continue to challenge the security of Cloud-based storage, with concerns that their sensitive and private data will be made available and accessible to online criminals.

However, when security concerns are exploited, they often stem from a flaw in constructing architecture. With a correct security development process, as well as the clear communication of governance procedures, enterprises can ensure that their data is made secure and private while remaining accessible.

Understanding master data management

While master data management is a fundamental aspect of any data strategy – allowing for consistency, reliability, and successful operations, it is often a misused and underestimated concept that can jeopardise the overall strategy and growth of an enterprise.

When improperly processed, master data management can seriously diminish the overall reliability of data, with no clear path to the most updated and qualified version of a master data source. As a follow-on effect, driving and optimising master data can also ensure more consistent operational functions in a business with greater visibility and version control of critical master data sources.

Learn more: Essential Data Management principles for your business

To improve master data management, more emphasis should be placed on overall communication and clarity, rather than optimised master data management platforms. With a clear culture of data management, as well as established governance processes, enterprises can ensure that their master data is reliable both in the short and long-term, while a focus on simply introducing more complex and capable platforms may ignore the underlying lack of acknowledgement and ethos.

Committed to empowering a data-driven approach

At DataShapa, we’re committed to enabling a data-driven approach, allowing enterprises to base their strategy and operations on actionable, trusted intelligence rather than on instinct.

From implementing Machine Learning frameworks to establishing a core Data Warehouse, our wide range of services ensures that businesses reach goals and access insights seamlessly, consistently, and reliably.

To learn more about how collaborating with a BI consultancy can enhance your data, discover how we’ve previously aided enterprises here. Alternatively, if you have any questions or enquiries about our methodology or approach to Business Intelligence and Data Analytics, contact us here – we always aim to respond as soon as possible.