Agile Ways to Transform How You Work with Data

To reap long-term benefits from data analytics, enterprises must prioritize agility, reusability and faster application delivery.

One of the most valuable currencies in business today is data. By tapping into streams of incoming data, organizations can derive proactive insights that can help drastically streamline operational processes, design more effective products and services, and better anticipate market demand.

Most organizations have recognized the clear business benefits that leveraging existing data can bring. Many are working tirelessly to incorporate a slew of various storage and analytics tools to quickly get the most value out of their data. Despite the widely recognized value and an ever-increasing arsenal of data-related software, many organizations – especially large enterprises – struggle to achieve data agility and organization.

To realistically reap the long-term benefits of data analytics, enterprises must rethink their traditional database and IT system processes, prioritizing agility, reusability and faster application delivery above all else. By ignoring agility and organization best practices, companies run the risk of wasting their data, which can result in lost revenue opportunities, lower operational efficiency and productivity, product and/or service quality issues, unnecessary data storage costs and more.

Six widespread data agility challenges

Research from Forrester indicates that between 60 and 73 percent of all data within an enterprise goes unused for analytics. There are several factors contributing to this unfortunate and concerning reality, including the following six data-related challenges that run rampant across the enterprise:

1. Antiquated Company Structures: IT departments are typically conservative by nature, perpetually trying to reduce costs and resisting changes to running systems. Historically, this department was cut off from the rest of the organization, yet all databases and/or data warehouses fell within IT’s jurisdiction. In many cases, they still are. As a result, it can be very difficult to incite change and motivate conservative-leaning IT departments to embrace newer, more agile data methods. Organizations should consider creating a specialized BI/Analytics unit – either under the CFO or in a separate CDO department – to open up the space for transforming the data environment and inject more agility.
2. Legacy IT Infrastructure: Given the time, resources and investment required to update massive, mission-critical systems, legacy IT infrastructure still remains within most enterprises. However, since legacy infrastructure wasn’t built to handle the extreme demands of today’s compute and data-intensive workloads, it creates chronic bottlenecks that significantly impact data pipelines and the performance of analytics. As a result, any related data reporting is often too slow to be useful for proactive decision-making.
3. Fear of Change: One of the biggest roadblocks to achieving optimal data agility and organization is the culture of natural resistance to a change. In most enterprises, employees aren’t familiar with new technologies or methodologies designed to optimize for agility and organization, such as DevOps, containers or microservices. It can be difficult to overcome this collective fear of change and in some cases, getting an entire organization to adopt agile methods as ‘the new normal’ can take years.
4. Data Fragmentation: Many organizations are unaware of where exactly their valuable data resides as it’s often scattered in data siloes across a variety of disparate legacy systems. This makes it nearly impossible to combine data streams for analysis purposes and can lead to poor data-driven decision-making that’s conducted without sufficient context.
5. Overly Simplified Analytics: To tackle huge data volumes within the limited constraints of legacy IT systems, many data analytics tools simplify the data being used to speed up results. Aggregated reports or reports based on data marts can be generated quickly and at times provide a decent overview of the current business situation, however they don’t provide a true understanding of the ins and outs of the organization and/or the markets in which it operates, which is ultimately the most critical use case of data analytics.
6. Data Privacy Regulations: The rise of data quality and governance regulations like GDPR and the California Consumer Privacy Act have made establishing clear data strategies essential. Having a dedicated team responsible for ensuring all data is relevant, correct and compliant is also critical; however, this poses an enormous challenge to most organizations. Many struggle to keep up with an ever-increasing amount of data and appease strict regulatory auditors, and it’s even more challenging for organizations with limited human and technical resources. That’s why it’s important to build a strong team to support a clear data strategy and its implementation

What data maturity requires

To overcome common data agility challenges and reach desired levels of analytics maturity, organizations should consider cloud. Cloud makes it easy to start something from scratch. Cloud platforms can also allow organizations to easily modernize their existing, legacy infrastructure and bring together previously siloed data, thereby avoiding any historical complexities and reliance on overburdened IT departments.

With making cloud-based technical improvements, organizations should also educate their employees on the value of data agility, and clearly outline why it’s so vital. Bringing this lesson to life by conducting recurring training programs or small research projects can work well. It’s also important to seek out new hires who consider themselves experienced and committed agile advocates.

Embracing agile data tools, best practices for long-term success

To thrive in today’s competitive business landscape, data must be at the heart of every organization, no matter its industry. Rather than focusing solely on data acquisition speed or instantaneous analytics results, prioritize thinking differently and modify data-related processes whenever necessary to overcome long-standing problems. Above all, recognize that having an agile mentality and instilling a nimble culture company-wide is paramount to success.

Dynamic, hybrid cloud-based technology is undeniably important in the quest to achieve data maturity; however, it can’t solve every data agility woe on its own. Consider technologies like in-memory databases, containers and microservices as helpful translators in the larger mission to instill a culture of nimbleness. In doing so, organizations can establish systems and teams capable of collecting and acting upon infinitely valuable data analytics.

With agile data tools and best practices in place, organizations won’t be forced to upgrade and replace their systems every couple of years. Instead, they can focus efforts on what matters most: making proactive decisions, delivering a competitive edge and maximizing market opportunities.