CTOs are tasked with figuring out how to create a data architecture that can scale with the needs of their business. But before they can help their companies use data to fuel business growth, they need to tackle a few data trends.
By 2022, organizations will have more data than ever before and less control over it. As data grows in volume and complexity, making that data available for insights is a barrier for 90 percent of enterprisesOpens a new window . Data silos are even more prevalent as organizations ingest data through various sources and formats without a consistent delivery method to share across the business. Chief Technology Officers (CTOs) are tasked with figuring out how to create a data architecture that can scale with the needs of their business, make the enterprises’ data useful, and facilitate the data analytics and reporting processes throughout the organization. CTOs are faced with a few different data trends that they need to tackle in order to help their companies use data to fuel business growth.
Trend 1 – Data growth means loss of control
Many CTOs feel that control of your data is an illusion. In the next few years, organizations will have more data than ever before and less control over it. While data has been the lifeblood of many organizations, it has also been an obstacle to growth. So, why are companies unable to use their data effectively? Here’s what we’ve seen:
- Data volume growth – More data has been generated in the last 2 years than what existed in the entire human history up to two years ago. One in ten professionals reportsOpens a new window that their data volumes are growing at 100 percent or more per month.
- Proliferation of data silos – It has never been easier to launch a new data silo in your organization. On-premise and “traditional†data sources can’t simply be ripped and replaced and the process to centralize that data is not an easy one. SaaS models that rely on guerrilla/shadow IT are just a credit card number away and open source is still often a route around IT blockages.
- Shifting standards and formats – Data has never been more open but harmonizing all the new formats is a constant challenge as enterprises look to reconcile new formats like Parquet and very loose semi-structured formats like JSON.
- Decentralized security – When data lives in silos, ownership, and responsibility become blurred. Systems have different security and permissions models which makes it difficult to move data down the pipeline and risk losing the permission system.
To break down data silos, businesses should store all the sources of data and harmonize them into data sets inside the cloud. Many data integration tools will have connectors to popular data sources that allow you to bring data together. Make sure to select a data integration that can help you leverage a cloud data warehouse to store and collect data for transformation needs as this will help you turn that data into insights.
Trend 2 – CTOs need to use data to innovate
The growing predominance of semi-structured data will create new challenges for CTO’s to be able to innovate their data pipeline. Unstructured and semi-structured data represents 85% or more of all data.Opens a new window With advances in machine learning, artificial intelligence and the Internet of Things (IoT), the amount of semi-structured data will continue to grow. This poses a problem for data professionals as semi-structured data is difficult to use to derive insights without the proper data management strategy.
A dual approach to data management, integrating both structured and semi-structured data, is the hallmark of a modern data management strategy. And a modern data strategy needs a modern, cloud-based data warehouse that can handle both structured and semi-structured inputs.
The growth of data —structured, semi-structured, or unstructured—creates an urgency for faster time to value from data insights. To keep up with the demand for reporting and the pace of data flowing into the organization, automating parts of the data journey is becoming increasingly important. Manually running jobs, workflows, and processes and hand-coding parts of the ETL process are no longer viable options for companies that want to innovate quickly. Invest in purpose-built, cloud-native solutions that require low or no coding so developers can automate parts of their data journey and start innovating with the time they saved.
Trend 3- The democratization of data skills
With growing data volumes and varieties, data integration skills will become commonplace. Everyone will be expected to be able to manipulate data to make decisions in their role.
Companies aspire not only to make decisions that are data-driven but to build systems that can make decisions. IT literate millennials arrive in the workforce with SQL skills expecting to not only have access to data but to easily manipulate it. This means putting data into the hands of users with a much wider range of job titles than ETL developer or data analyst/scientist. A self-service data lake can empower users by giving them a huge catalog of data to look at.
Enterprises need to start thinking about how the democratization of data skills will impact job roles and responsibilities. One option is to open the self-service model to other teams within the organization. Insights-driven teams like marketing and finance can leverage lightweight data integration tools to centralize their data for analysis. Vendors that provide a simple tool that can power data integration quickly, and at scale, give non-technical users a dashboard view to monitor data pipelines for metrics tracking and reporting. For technical users, loading data into a cloud data warehouse then transforming raw data into analytics-ready data can help them serve their wider organizations with real-time reporting.
Modern data management can provide a better night’s rest
CTOs have a lot on their minds and on their plates. Breaking down data silos within the organization is the first step into creating an insights-driven organization that uses data as their competitive advantage. Leveraging modern technology that is purpose-built for a cloud environment can reduce the complexity of data integration and data transformation. This will help to free up developer time for innovation and help other teams in the business to self-serve their own reporting needs.