Businesses today face significant challenges unifying, analyzing, and interpreting the vast amounts of data from various points they collect and turning them into insights and then effectively into actions. They are, in fact, searching for next-generation architectures that can attend to divergent data assets, accelerate data processing and analytics, and drive innovation with lower costs.
One of the best ways to tackle these challenges within organizations at any scale is to build a data lake that helps organize and analyze data in any format and from any source, answer unknown questions, explore trends, and make informed decisions.
Precisely, “a data lake is a scalable and secure data platform that allows businesses to ingest, store, process, and analyze any type or volume of information,” as described by Google Cloud.
Moving on from why you need a data lake, we will be looking into how you need to plan your data lake in this article.
As an analogy by Google Cloud, “Data Engineering is like Civil Engineering”
- Raw Materials need to be brought to the job site (into the Data Lake)
- Materials need to be cut and transformed for purpose and stored (pipelines to data sinks)
- The actual building is the new insight or ML model etc.
- The supervisor directs all aspects and teams on the project (workflow orchestration)
“A smarter way to jump into data lakes” article by McKinsey&Company states that a well-maintained and governed “raw data zone” can be a gold mine for data scientists seeking to establish robust advanced analytics programs. In addition, as companies extend their use of data lakes beyond just small pilot projects, they may be able to establish “self-service” options for business teams where they can generate their own data analyses and reports.
Businesses require different time frames and capabilities to grow their data lakes based on their strategic company objectives and technical qualifications at the beginning of the project. It is essential to underline that businesses should adopt an agile approach to their data lake design and rollout, observing various technologies and management approaches and testing and improving them before reaching optimal data storage and access processes. With the right approach, companies can bring analytics-driven insights to the market much faster than their competitors while significantly reducing the cost and complexity of their data architecture.
During data-lake development, it may be inevitable to get embroiled in details and lose momentum at some stages, leading to losing focus and diving into other “urgent” projects. To prevent this, businesses must continuously answer questions concerning their data set size and variety, their existing data management capabilities and big data expertise, and product knowledge in the IT organization pertaining to the relevant development stages.
These questions include:
- Where is your data currently stored?
- What is the total size of your data?
- Where will you store your data?
- How much do you need to transform your data? (EL/ELT/ETL)
- How advanced are the current analytics tools?
- Do you have traditional/modern development tools and methodologies?
- Do you manage workloads dynamically?
- How many concurrent data users do you generally require?
- How fast do end users need to access the data?
Let’s review some fundamental steps in the data lake development process.
1) Identify your data sources
Before building a data lake, your organization must thoroughly analyze its internal and external data, including the data sources, data types, data formats, data schemas, and total and incremental data volume. This way, you can take a picture of your data organization “as is” and clarify data points, user roles, permissions, and service methods. This stage requires strong communication between departments and perfect documentation.
2) Ingest your data
This step is around ingesting structured, semi-structured, and unstructured data into your data lake.
You must obtain and import data for immediate use or storage in a database and decide which data to stream in real-time or ingest in batches. When data is ingested in real-time, each data item will be imported as the source sends it. When data is ingested in batches, data items are imported in chunks at periodic intervals. Then, it is vital to prioritize the data sources. McKinsey&Company article states, “Ideally, the population of the data lake should be based on the highest-priority business uses and done in waves, as opposed to a massive one-time effort to connect all relevant data streams within the data lake.”
While data ingestion technology offers various benefits, such as more efficient data management and competitive advantage, an ever-growing number of data types, data privacy regulations, and data security remain challenging.
It may become complex to ingest data at a reasonable speed and process it efficiently when there are numerous big data sources in diverse formats. In such cases, you can use software to automate the process.
This step is critical as you need to build fast and accurate ingestion and get uncorrupted raw data into storage to have a properly functioning data lake.
3) Clean and organize your data
Data from multiple systems have the risk of containing errors, duplications, omissions, or just being irrelevant. Finding relevant data helps to ensure that your analytics tools deliver actionable insights for business decision-making.
This step includes fixing and removing common data errors, including missing values and typos. A Harvard Business Review study discovered that only 3% of companies’ data meets basic quality standards. The same study states, “Bad data wastes time, increases costs, weakens decision making, angers customers, and makes it more difficult to execute any sort of data strategy.“
Data cleaning is essential for having high-quality data and is key to combining data in more meaningful ways to serve reporting and dashboard queries. Synchronizing different data requirements and standards can be messy. Unless you have clean data, your business intelligence and analytics efforts are deterred, and you will retain a severely restricted operational efficiency due to unreliable results.
A study by KPMG states, “High-performing organizations have begun to master data quality issues, and see it as much less of a challenge than others: while data accuracy and quality ranks as the top challenge for companies overall, for high-performing companies, it falls near the bottom of the list.”
4) Define use cases and prepare data for queries
While data use cases are different for every organization depending on the overall business strategy, you will need to start with defining data objectives and use cases for your organization.
This step entails making your data ready to feed the most valuable use cases for your objectives.
Some frequently utilized and common use cases include:
- Employee engagement improvements
- Customer acquisition improvements
- Delivering a more personalized customer experience and recommendations
- Price optimization and forecasting
- Smarter and more personalized products/services development
- Fraud prevention and cyber security
- Predictive maintenance
Once you define your use cases, you can start writing your queries.
5) Visualize data
Once your data is collected, processed, and modeled, you must visualize it to make conclusions.
Visualization is paramount to advanced analytics. By translating information into a visual context, you can identify patterns, trends, and outliers in large data sets and assure that models perform as intended. Especially with complex algorithms, visualization makes it easier to decipher than numerical results.
Data visualization provides a quick and effective way to specify areas that need improvement or more attention by making data more expressive for stakeholders.