In this special guest feature, Craig Kelly, VP of Analytics at Syntax, discusses how data lakes can help companies better analyze and use the mounds of data they already store. Craig heads up professional and managed services around analytics and product and application development for the analytics practice. Before working at Syntax, Craig was a co-founder of EmeraldCube Solutions. He has been in the analytics space for the last 20 years, working with IBM Cognos, Oracle BI, GoodData tools to build solutions for ERP customers. Craig and his team now focus primarily on AWS analytics, integrating traditional data warehousing and BI, along with forward-looking ML and forecasting capabilities.
Last year, nearly half of IT leaders (42%) deprioritized data analytics and business intelligence (BI) initiatives because of shifting priorities. Now that businesses have adjusted, data analytics and BI are rising back to the top of their list of priorities. 55% of businesses plan to invest in data analytics and business intelligence technology this year.
As companies undertake efforts to become more data-driven, they need to be strategic from the very beginning. If your organization isn’t deliberate about how it stores and analyzes data, you won’t generate insights that help you outperform the competition. Data lake technology is helping cutting-edge organizations take control and generate value from their data.
Data is growing, but that doesn’t mean insights are
Every IT leader knows the data we produce and collect is growing exponentially. By 2025, nearly 60% of data will be created and managed by enterprises — double the amount they produced from 2015. The average company now manages 33 unique data sources.
The volume of data and data sources means it’s no longer an option for companies to rely on spreadsheet-driven storage and analysis. Spreadsheets provide a limited and backward-looking review of your data, are prone to inaccuracies and are time-consuming to maintain.
Every business claims they want to be data-driven, but because of the perceived hassle, many fail to go beyond collection and storage. Between 60% and 73% of all data in an enterprise is never analyzed. Why bother spending time and money collecting it if you’re not going to use it?
Untouched data is a missed opportunity to drive profitability, operational efficiency and business transformation. With more data than ever coming from disparate sources, enterprises need a smarter, more efficient way to manage the information they collect.
4 ways data lakes can help you become more data-driven
IT leaders should consider data lakes as a possible solution for both data management and analysis. A data lake is a centralized cloud storage area that can house large amounts of raw data in its native format and from multiple sources. Aberdeen found that organizations with superior data lake practices experienced a 9% boost to their organic revenue growth. Data lakes’ benefits include:
1. Centralized repository: Analyzing data from only a few sources limits the insights you can develop. The average company grows its data sources by 50% each year, and the most competitive businesses are using this abundance of information to their advantage.
Data lakes consolidate information from multiple sources across the business — like your ERP, CRM, HR systems or IoT devices — regardless of whether it’s stored in the cloud or on-premises. Data centralization increases data accuracy, reduces data silos and eliminates manual data entry, enabling your team to spend more time on value-add activities like analysis.
2. Convenient access: One of the most appealing benefits of a data lake is its ability to help users quickly and conveniently analyze a wealth of data. Businesses with leading data lake practices are three times more likely to report a “strong” or “highly effective” go-to-market process as a result.
Because data is stored in its native format, data preparation, retrieval and analysis are much less burdensome with a data lake. There is little required for data preparation, unlike spreadsheets or data warehouses where the data entered needs to be standardized. Data retrieval does not require predefined parameters for search, making it easier to access and extract data.
3. Cost-effective: In contrast with a data warehouse, which stores data in a hierarchical manner via files or folders, a data lake uses a flat architecture. Organizations can scale storage as they grow, which is more cost-effective and easier to implement, meaning no big capital outlay and no waiting months for development.
Adding data lake technology to your ecosystem also improves the functionality of existing legacy systems by offloading capacity. This is especially appealing for larger, more established enterprises that have made significant prior investments into data warehouse and mainframe technologies.
4. Modern capabilities: Data lakes allow enterprises to use more advanced and sophisticated analytical techniques. Organizations can apply machine learning and AI to clean and augment incoming data, run complex algorithms to correlate different sources of information, or apply predictive analytics. Insights become more mature — yielding even more value for your organization over time.
But be aware that without the proper governance and processes, a data lake has the potential to become a data swamp. If left unmanaged, a data lake can deteriorate to the point where it is inaccessible to end users. Work with a trusted advisor to ensure clear protocols and responsibilities are set from the very beginning.
Empowering better decision-making and growth
While businesses are generating more rich digital information than ever before, simply having data doesn’t equate to growth. Organizations need to leverage advances in cloud computing to facilitate more efficient and complex means of data storage and analysis. Data lakes enable organizations to collect more data, from more sources, in less time, at a fraction of the cost. With proper implementation, these data storage systems can yield stronger business analytics and faster decision-making, empowering your organization to become truly data-driven.
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