The Big Data Transformation: How Cloud Data Tooling Enables a Company’s Modern Data Lifecycle
The 2010s could be described as the decade of data. Using a smartphone to go online was on the rise, and social media sites such as Instagram and WhatsApp were the must-have platforms for connecting with others.
The technological landscape shift brought on an abundance of data in a short amount of time. In 2010, 2 zettabytes of data were created globally, with only 9% of that data available in a structured format. Fast forward to the end of the decade, and by 2019, the annual data volumes had skyrocketed and reached 41 zettabytes. However, still, only 13% of that data was structured.
Along with the explosion in data volumes in the 2010s, a plethora of tools and technologies emerged to help make sense of it all. However, no standards existed, and it seemed that every day a new framework or approach was being touted as the solution to everything – think Apache Hadoop, Cloudera Impala, Cassandra, etc. Luckily the landscape has matured since then, and various descendants of these early tools and technologies have found their way into everyday use during the 2020s – not only for solving unstructured big data but also in the simpler structured data workflows that many companies still use. This development has opened a new realm of wide-ranging possibilities, especially for the average company.
Organizations of all shapes and sizes are realizing that they do not necessarily need to possess big data to take advantage of the benefits of this new era of tools. But, while it’s an exciting time for companies, many are unsure how to best capitalize on these tools.
Here we share some key insights to help along your data enablement and delivery journey and, in so doing, take full advantage of a matured and highly functional toolset at your disposal.
THE SHIFTING DATA LANDSCAPE
In a traditional approach, companies use centralized teams to ingest, curate, and manage data. In this day and age, they are dealing with a significant volume of operational and analytical data from a growing number of disparate applications, databases, data warehouses, analytics systems, and so much more. To add to this, the central teams are often stretched and overworked while they play to the needs of an ever-increasing and diverse population of data consumers.
Additionally, the technical literacy of the average business stakeholder has increased. Think Microsoft Excel in the 1990s and compare that to now, where almost all business users can probably develop a well-thought-through spreadsheet. Tools such as Power BI are becoming more user-friendly, leading to a democratization of data that is accessible to everyone.
This onslaught of data, combined with an increasing need across the business, has created problematic bottlenecks for these central teams. Companies recognize that their legacy data tooling and processes are ill-equipped to handle the ongoing shifts in the data landscape. The traditional approach is having a negative impact and resulting in missed revenue opportunities, lower efficiency, and issues with productivity and quality – this is because we cannot easily equip a tech-savvy workforce with the data and tools they need to innovate.
HOW BIG DATA TOOLING MITIGATES CHALLENGES
The industry usually defines big data by the 3 Vs: volume, velocity, and variety. But regardless of the scale of a company’s data across the 3 Vs, it can still ease its data challenges by leveraging what these new tools in a modern data stack (MDS) have to offer.
The traditional approach – and with it, the traditional data stack – should be strategically phased out in favor of the modern data stack. The MDS is a composable ecosystem of data tools that emerged out of the wild west of big data tools from the 2010s. An MDS differentiates itself from traditional tools by focusing on cloud-native technologies for storage, computing, and scalability.
When designing a modern data stack, consideration should be given to the following.
- Flexible ingestion options are important so we can integrate with a variety of sources, such as on-premises systems, cloud ecosystems, or SaaS offerings and access data in a streamlined fashion.
- Scalable and cost-effective cloud storage keeps data unified and centrally accessible at an optimal cost.
- A variety of computing options enable organizations to work with numerous volumes and varieties of data and scale operations on demand to suit these needs.
- Discovery and downstream process enablement where data is made available for the generation of traditional business intelligence and visuals, ad-hoc analysis, or served up for machine learning and advanced analytics.
THE ADVANTAGES OF MOVING TO A MODERN DATA STACK
So, what’s the secret sauce to an effective Modern Data Stack? It is implementing an effective mechanism that allows different business users to execute different use cases, all leveraging the same integrated, governed data sets, modeling and conforming these virtually to suit their needs with minimal help from any central IT team, all with standard governed tools to accomplish a unified business strategy.
By taking a modern approach, critical operations that were previously handled by the centralized teams are spread across the enterprise. The effect is that business users are empowered and free to experiment, ultimately resulting in new insights and opportunities for the company.
After implementing an effective MDS, organizations will start to see real value which results in a domino effect throughout the business. The bottleneck on IT becomes significantly reduced. Speed and agility are accelerated, leading to improved efficiencies and decision-making. The flexibility to innovate is enhanced as new ideas that require new data are managed within the business unit.
CASE STUDY: SUCCESSFUL IMPLEMENTATION OF A MODERN DATA STACK
Kenway helped an asset management company implement a modern data platform that transformed its disconnected analytics program and established a common source of truth.
The company struggled to understand the full breadth of its sales team’s relationships with current and potential wealth advisor clients. Without a data solution for invaluable insights and analytics, the company lacked clarity around advisor profiles, such as the universe of products being purchased, the percentage of sales versus competitors, and engagement with the organization’s sales and marketing representatives.
Kenway collaborated with the asset management company to create a future state platform with best-in-class Data Architecture, Data Management, and Data Governance capabilities. The platform solved the client’s immediate issues around targeting and lead qualification while providing long-term extensibility for future use cases. With a complete view of its clients, the asset management company achieved better business outcomes around its sales, marketing, and product distribution efforts.
HOW KENWAY IS POWERING THE DATA TRANSFORMATION
Having the right tools is just one part of the data enablement journey. An organization must also consider its data strategy through the lens of the alignment of people, processes, and technology. Kenway Consulting aims to holistically partner with companies along the data enablement journey. Kenway’s approach includes:
- Assessing: Kenway identifies how the organization invests in the applied uses of its data.
- Enabling: The initial build and release of the data platform.
- Empowering: Kenway provides platform training and documentation to internal teams.
- Sustaining: Support for platform operational processes is decreased and transitioned to the organization.
Connect with us to learn more about how the Modern Data Stack can help your enterprise succeed.