Good decisions are based on good data. Data and analytics power business insights, and they’re the backbone of artificial intelligence (AI) solutions that amplify better business decision-making. They power C-suites and algorithms alike. The challenge? There’s more data than ever before. But, data lakes can help your business store data as is, and therefore make more data available for analytics, big data processing, machine learning and other uses.
Traditional databases and on-premises storage can only go so far with the immense amount of data generated day to day and minute to minute. Data silos, which arose in the early internet era, helped manage several different types of data, but these silos were not organized together in a way that led to good insights.
From data silos to data warehouses
A data warehouse is infrastructure that allows businesses to bring together and access various structured data sources, the kind that would have been managed with different silos in an earlier era. Structured data is standardized, formatted and organized in a way that’s easy for search engines and other tools to understand. Examples of structured data include addresses organized into columns or phone numbers and health records all coded in the same way. In short, data warehouses are organized, making structured data easy to find. However, they have a hard time dealing with unstructured data.
These days, data comes from a variety of sources — both structured and unstructured. Unstructured data includes clicks on social media, input from IoT devices and user activity on websites. All this information can be extremely valuable to commerce and business, but it is more difficult to store and keep track of than structured data.
Hadoop and data lakes
In the early 2000s, Apache Hadoop, a collection of open-source software, allowed for large data sets to be stored across multiple machines as if they were a single file. This allowed for large amounts of unstructured data to be handled and analyzed more easily and was the beginning of data lakes.
Unlike a data warehouse, a data lake is perfect for both structured and unstructured data. A data lake can manage structured data much like databases and warehouses can, but it can also handle unstructured data that is not formatted or organized in a predetermined way. As unstructured enterprise data grows and grows, data management is now a business imperative. Data lakes are an effective way to store diverse data and can scale up to petabytes and beyond.
And, you don’t need a specific structure schema for data to flow into the data lake. Just as rivers, streams and other waterways flow into a lake, data from across the business environment can easily flow into a data lake.
Data lakes deliver many benefits to businesses, such as:
Storing large amounts of unstructured data in one place has its challenges. If a data lake lacks standards or governance, it can quickly become a data swamp. Data swamps may be rich with information, but are poor with insight. Dirty data can hold a lot of information, but it’s not useful until it’s cleaned with good data management. Because of the lack of structure, it’s difficult to glean value from a data swamp — leaving useful insights buried in its depths.
A data lakehouse provides structure and governance to data, but the data lake can still ingest unstructured, semi-structured or raw data from a variety of sources. A data lakehouse also brings together both the strengths of the data lake and the data warehouse on one platform, making the contents of a data lake more accessible to data scientists, AI and any other person or resource that can make use of it.
Every industry relies on data. Here’s how yours can benefit from using a data lake to store and manage data.
Data lakes in healthcare
The healthcare industry is the single largest source of data on earth. That data ranges from a single patient’s heartbeat or oxygen levels to large-scale studies of cancer and other diseases. Whether in a clinical or research situation, healthcare data comes from a variety of sources, in a variety of formats, and needs to be accessed by a variety of users. With their ability to ingest unstructured data, data lakes can better handle the diverse types of data the healthcare industry uses than more traditional data storage strategies.
Data lakes in the public sector
Public sector organizations such as governments, municipal services and agencies collect and organize a variety of data — including census data, public records and data regarding public services like electrical grids. Much of this data does not have a unifying schema, and data lakes make storage of this unstructured data much easier. Access to good data allows public officials to gain insights about population dynamics, utilities, crime rates, migration and services, and allows policymakers and experts to make informed decisions about laws, regulations and standards.
Data lakes and manufacturing
Manufacturing relies on big data and real-time insights about supply chains, electricity costs, transportation and countless other phenomena. Those data flows translate into billions of dollars of activity, with manufacturers routinely making decisions based on good business intelligence. Data lakes can turn a flow of unstructured data into a valuable source of insights and analytics.
Data lakes and finance
The financial sector increasingly relies on AI and machine learning. For example, algorithmic trading requires data sets that inform traders about which stocks to buy and sell and helps traders discern where potential value will grow. These decisions happen in fractions of a second and constantly draw on the data contained within a data lake. At the same time, each trade and transaction generates new data that flows into a data lake.
Data ingestion, storage and management (and therefore data lakes) matter across use cases. Here are a few real-world success stories where data lakes are playing a key role in driving business differentiation.
Hailing from San Francisco, Sunrun has been a leader in the solar power industry since 2007. The company needed to update, streamline and simplify its data architecture, reporting and visualization. Informatica helped the solar company migrate from on-premises data storage to cloud infrastructure with a data lake and data warehouse model. The new model saves time for Sunrun’s IT professionals — reporting and visualization tasks that once took multiple quarters are now executed 3x faster.
BC Hydro and Power Authority is British Columbia’s main electricity distributor, serving approximately 1.8 million customers. The utility company wanted to help their customers monitor electricity consumption in close to real time and reduce the total cost of ownership for their infrastructure.
By implementing data solutions, which included Informatica data lakes, BC Hydro was able to give their customers rapid insights about their electrical consumption. Because these data solutions were scalable and could handle especially large data sets, the utility was able to give its customers insights on power usage in real time, all while lowering costs and protecting data integrity.
Data lakes are only one part of a large data ecosystem. Intelligent and automated data management incorporates data integration, data quality and metadata management. A visual representation of data and data usage allows you to easily and efficiently keep track of your cloud data management.
Informatica offers end-to-end solutions for data management that include ingestion, integration and AI-powered data governance that prevents data lakes from degrading into data swamps. Informatica solutions are vendor-agnostic and offer an intuitive, UI-based approach that requires no hand coding.
Hadoop was the first platform to support data lakes with an on-premises and cost-effective model. Early data lake platforms were not scalable and limited in what they could accomplish. Since the earliest days of on-prem data lakes, various models and platforms have expanded to cloud storage.
Amazon Web Services (AWS) was the first cloud-based data lake, allowing customers a greater degree of scalability and flexibility. Other services like Azure Data Lake were quick to follow, and they all took advantage of cloud storage and computing to offer businesses quality data management and data preparation.
Different platforms can offer specific services for different data types. Informatica for Google Cloud optimizes the value and insight of Google Analytics and integrates easily with other Google properties such as AdWords and YouTube, allowing users to manage metrics from the full range of the Google ecosystem.
Data management and governance is a crucial element of business success, whatever your industry, and data lakes are a powerful way to manage and glean insights from data. Find out how Informatica can help you make the most of your data and turn information into insight.