Here are 10 recommendations for developing strategies to reduce delays in data integration and management and increase business value through faster BI and analytics.
Deliver more timely data and recommendations to users. Organizations need to modernize the paradigm for BI, visual analytics, and dashboards, particularly where they are deployed to operational managers and frontline personnel. Such users often need very timely data, often times including real-time data views and analytics within the context of their responsibilities. They also need applications that can supply users with recommendations about data sets that might be relevant, visualizations, analytics, and ultimately suggested actions to take. Operational managers and frontline users would then be in a better position to make good decisions based on fresher, more contextual, and richer information.
Find the right balance between agility and centralization of management. An imbalance here leaves neither users nor IT happy and can lead to bottlenecks that throttle faster decision making. Users want agility, and organizations are pursuing self-service technologies to give users more freedom in how they personalize workspaces and access and analyze data. Ungoverned self-service can lead to too many data silos, duplication, and workloads that haphazardly compete for computation and processing. IT’s perspective is to ensure good governance, performance, and quality, especially for priority workloads. Clamping down unnecessarily on users will make it harder to move forward and drive users to set up their own data silos in the cloud. Data virtualization solutions could be helpful in reducing dependence on traditional physical data consolidation, which tends to require rigid, preset integration processes. Users and IT need to have open forums or a center of excellence to discuss how to balance self- service with centralization.
Explore how agile, DataOps, and related methods could help projects deliver value sooner. Organizations are often mired in chaotic, inconsistent, and even redundant work in projects for developing BI, analytics, applications, and AI. The results are delays, inefficient use of data and processing resources, and dissatisfaction among users, who need capabilities as soon as possible. Organizations that are using agile methods, as well as DevOps, DataOps, and design thinking are having positive experiences. Organizations that are not using them should try these methods for one or a small number of projects that have clear deliverables, to assess whether they are helpful and iron out difficulties before trying them on a larger number of more complex projects.
Focus on improving data preparation, transformation, and pipelines. Delays, bottlenecks, and inconsistencies in these areas slow down decision making and force users to spend more time sorting out problems with the data than analyzing it to answer business questions. Organizations need to evaluate new technologies, many of which apply AI and analytics to preparation, transformation, and pipeline development, to reduce latency and enable users to know more about the data and more quickly address problems. Advances in tooling combined with use of agile and DataOps methods can improve collaboration and continuous improvement of data so future cycles are more efficient.
Get the big picture and orchestrate parts into a whole. As projects grow more numerous and workloads become more complex, it’s easy for organizations to get bogged down. Despite having the latest technologies, it can seem impossible to deliver data faster to support faster analytics. Methods such as DataOps can help organizations get a holistic picture and gain an end-to-end understanding of interrelated steps in projects and stakeholder responsibilities. Delivering timely data and recommendations to users and finding the right balance between agility and centralized management are key to taking self-service analytics to the next level. Organizations should evaluate cloud services and tools that not only improve data life cycles but also help them orchestrate what happens in multiple data pipelines and observe the big picture.
Utilize automation when possible and reuse. With an increasing number of analytics and AI workloads needed to meet diverse business demands, it’s essential to exploit the potential for smarter automation and reuse in software solutions and cloud services. Organizations heavily dependent on manual coding, monitoring, data preparation, and integration will struggle to scale as more users and applications need to interact with the data and test models and algorithms.
Create knowledgebases about the data and improve access to them. Such resources are valuable to users, administrators, data scientists, and applications as they can shorten paths to finding and interacting with all data relevant to a subject of interest. Data catalogs and metadata repositories are essential for data governance as well. Technologies are making it easier to develop and use these systems. Organizations should make it a priority to invest in these technologies so they have useful resources of knowledge about their data that users and applications can easily apply to produce more complete views of and access to the data.
Improve employee trust in data, analytics, and AI. It doesn’t matter how fast the data, analytics, and algorithms are if no one can trust the data. Problems with data quality are key challenges stalling progress in building strong analytics cultures and accelerating decision processes. Data trust is essential to collaboration on decisions and acceptance of analytics insights. Organizations need to invest in data quality, shared data catalogs, data lineage, and other technologies and practices that give decision makers transparency into the data and confidence in insights drawn from the data.
Use appropriate technologies to streamline governance. With goals for faster data and analytics, it’s never been more important for organizations to set up rules and policies to protect sensitive data and reduce confusion about where the data is, where it came from, who is accessing it, and what’s being done with it. Semantic data integration, which builds on a foundation of central data catalogs and metadata management, can help organizations answer these questions. Organizations should examine options for how a data virtualization layer could help protect access to sensitive data distributed across hybrid, multicloud platforms.
Evaluate the potential of data streaming and real-time analytics. With a greater selection of open source programs and frameworks as well as the latest generation of commercial tools to choose from, data streaming and real-time analytics are poised to become mainstream, possibly displacing older technologies and practices. Organizations should develop a strategy for how to augment existing data management and analytics with data streaming and real-time analytics and which business objectives would benefit. Accurate, accessible, and trustworthy data is critical for any organization to succeed. But the truth is, data alone doesn’t drive your business. Decisions do.
It is far more effective to lay out a narrative to present to senior executives or a board of directors than it is to inundate them with endless stats and charts. Curate your insights into a storyboard, stepping viewers through the data and its meaning.
The key is incorporating analytics into all your decision-making processes. That way you make the best choices every time even when making thousands or millions of them each day.
Choice leads to innovation and creativity. After all, data and analytics strategies won’t succeed without the ability to use a wide variety of techniques to develop processes that work best. For more information as to how you can improve and protect use of your data assets contact the data professionals at RStor.