Using data ineffectively is a long-standing problem for businesses at any scale that have failed to fully adopt digital transformation within their organizations. These businesses either use legacy and disparate systems across silos or utilize insufficient tools and software, making it increasingly difficult to collect, store and organize data in a way that is accessible and usable. According to Dimensional Research’s study, 90% of data professionals highlight that their work has been slowed by unreliable data sources, and almost two-thirds of data workers are impacted by having to wait on engineering resources while their data is cleaned and prepared.
Outcomes of the lack of a modern, unified analytics data platform and inability to effectively utilize data vary by industry. Some of the challenges businesses face include:
- In mobile gaming, companies fail to consolidate their internal marketing spend dataset and user acquisition and behavior insights, resulting in inaccurate ROI results
- In digital media, businesses have difficulties uniting and reconciling data they collect from payment gateways, analytics, and CRM tools, leading to poor customer experience and substandard fraud detection
- Retailers and e-commerce businesses fail to understand the unique needs of their customers and, therefore, are not able to offer personalized campaigns and recommendations
- In consumer goods, large-scale multi-brand conglomerates experience challenges in having a unified perspective of performance, resulting in siloes and organizational inefficiencies
It is also apparent that the questions businesses need to answer and the types of analyses they need to run change rapidly amid technological advancements and continuous changes in consumer behavior.
One of the ways to tackle these issues and unlock the full potential of your data is through data lakes. Data lakes, in simple terms, are central depositories designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data. Data lakes can store data in its native format and process any variety of it without size limitations.
James Dixon, the founder at Pentaho, explains the data lakes like this:
“If you think of a datamart as a store of bottled water — cleansed and packaged and structured for easy consumption — the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”
Unlike data warehouses which are better suited for a pre-defined set of questions to be answered, data lakes store unprocessed and unfiltered data in its original format, which enables users to have discoverability across multiple data types and explore rather than present preset insight.
In addition to being a relatively low-cost solution to store any and all data, data lakes have certain benefits to businesses at any scale.
Data lakes allow various roles within your organization, such as business analysts, product managers, data engineers, and developers, to access data with their choice of analytics tools and frameworks. Data stored in a data lake can easily be copied and accessed by different users. While you can provide broader accessibility, you can also control the data access within your organization. Also, with the right visualization software, any member of your business can monitor, analyze, and review business performance metrics or metrics specific to business units.
Simplified data management
A data lake ingesting every type of data eliminates the need for data modeling at the time of storing them. Therefore, you can filter and model your data when the needs arise. It also can store multi-structured data such as store logs, sensor data, social data, and chat data from a variety of sources, making it more versatile compared to traditional ways. Beyond these, you can access every piece of information from one single platform, driving efficiency across your organization.
Accelerated machine learning
Data lakes can handle the volume, variety, and velocity of data from different sources in any format and, therefore, can accommodate the large amount of data that Big Data, ML, and AI require. They enable businesses to run complex queries and deep learning algorithms to recognize patterns, answer unknown questions, and explore.
To illustrate the impact of data lakes, it is worth mentioning one of our success stories.
For beIN Asia Pacific, a multi-platform sports media company offering a stellar line-up of live sports events, we developed a technology stack neutral data lake that is highly cost-effective, flexible and can be queried by non-technical staff. This made it possible to aggregate inputs from various sources, such as app data and transactional data, and convert data in a variety of formats into a standardized format. By using this data lake, we then built a highly scalable business intelligence and data insights platform with customized dashboards for churn analysis, cohort analysis, user behavior analysis, and predictive modeling.
Please check out our website to read through our success story.
If you would like to hear more about our solutions, please contact us.