Big Data Modeling: Key strategies + Segment Solutions

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Big data is more complex than traditional data, and it requires special care to get the most insights possible from it. One way to ensure no opportunity remains undiscovered is through data modeling. However, big data modeling has special considerations to ensure it reaches its fullest potential.

Understanding big data modeling

Data modeling has traditionally meant the process of organizing data in a manner that fits a business's overall goals and objectives. It's done through a logical mapping of how data should flow, be processed, and work relationally with other data in the organization. Then, it can be executed as the physical infrastructure that the company actually uses.

However, with big data, data modeling has changed. It doesn't use the same static or slow-growing information as traditional data models, so the planning needs to be flexible and account for unpredictable growth. It's also often processed through automated means, including AI and ML models, which change frequently and may have a variable effect on data insights.

Since so many business insights come from data, it's often a worthwhile investment of time and resources; companies that excel at big data modeling may have the edge over their competition.

Schema design 

Unlike traditional data, which is highly dependent on structured schema (SQL), big data uses flexible schema, such as "not only SQL" (NoSQL), to share data between non-relational databases. This supports unstructured data and structured data alike and is easily scalable to include new data sources that wouldn't fit into a traditional SQL model.

Data formats and types

Because of its flexible schema design, big data models can compile data from a variety of sources, including purchase history, customer service chat records, SMS campaigns, website behavior, and onsite search patterns. All of this data has different formatting, file types, and data values, which may make it difficult to use together under a traditional data model. Big data models should provide a plan for data mapping to help these structured and unstructured data types play together nicely.

Data partitioning 

Partitioning helps break up large volumes of data into digestible chunks. Many frameworks like Apache Hadoop and Spark work better with partitioning so that distributed processing remains efficient and can handle real-time data more adequately. Data partitioning has been credited for big gains in speed and volume by companies like Netflix and plays a role in keeping financial data for different companies and accounts separate for security and compliance purposes.

Partitioning also helps eCommerce companies serve customers by geographic region, connecting them to the closest server or shipping warehouse for optimal shopping experiences.

Data indexing

Data indexing helps organize data into logical groups for more efficient retrieval and processing. It can be organized by any number of characteristics, but ideally, the most frequently used criteria. AI has advanced this to new levels and has taken away the need for humans to scour through the data for commonalities. Instead, the AI detects patterns and determines how the data is related before indexing it appropriately.

Data index makes it possible to conduct very specific search queries from a large dataset, for example, and get the right results.

Data governance

Data governance is the plan for how data will be handled within the big data model. It establishes policies and procedures for data, from collection to disposal – and everywhere in between. It identifies the key players in data governance and who is responsible for what while defining the value data has for the organization.

Real-time processing

Data has more value when it's fresh, so real-time data processing is often a prerequisite for any big data model. Real-time data is more useful for solving big data challenges. How? With AI and ML models trained to understand normal patterns, they can flag anomalies in real-time data as it comes in and may help improve data quality or security. It's also able to offer insights into the data processes themselves, giving engineers a clue as to how to better their workflows to get more from their datasets.

Choosing the right big data modeling tools

Big data modeling is currently a work in progress, with the best practices changing almost daily. However, picking the right tool early on grants you access to the latest and greatest in modeling as it happens. It can help you start out strong while creating more value from your data.

• Pick the right tools for your data, whether it’s structured, unstructured, or a combination of both. Your data volume, variety, velocity, and veracity will also play a factor.

• Choose a tool that can handle streaming data and can process it in real time. The best insights happen when the data used is fresh and represents what's currently happening with customers, inventory levels, financial accounts, etc.

• Consider your existing infrastructure. Will you have data warehouses that you'll be using alongside cloud-based tools? Do you need ETL capabilities that bring data insights back into your own business analytics dashboards? If you rely on Snowflake, for example, choose a data tool that can add additional value to what you already use.

• Check for quality assurance capabilities. You won’t be able to scrutinize every piece of data manually, and a good big data tool comes with proper auditing options. See that when you do find a data error, you can pinpoint the source and take action right away.

• Ask about security and privacy options. Organizations have a responsibility to take care of every piece of data they collect, and any tools integrated should follow best practices for data handling. Make sure your new big data modeling tool offers controls for different access levels and allows for the quick deletion of an entire customer profile across all channels.

Overcoming challenges in big data modeling

The millions of data events captured in a big data model all create value for an organization, but they also come with some risks to the larger system. To ensure the data pool stays current and of high quality, these challenges should be addressed at the planning stages.

Handling data volume and complexity

Big data modeling often integrates dozens or even hundreds of separate data sources and tools, making the sheer amount of data something to contend with. The proper infrastructure can both physically store and process all that data, as well as reconcile various data types. This includes a combination of structured and unstructured data.

Ensuring data quality

Data is only useful if it can be trusted. Data veracity matters more with large data pools, so having checks and balances on how data is sourced is very important. Not only does bad data negatively affect insights, but it can ruin any personalization in marketing campaigns, customer service outreach, and even financial records. Relying on internal experts to help fuel unique data approaches can be the best way to keep data quality at its highest level.

Big data modeling made easy with Segment

Big data modeling can seem like a giant undertaking, and for many, it is. Having the right partner with proven processes already in place, however, makes all the difference for starting out right and building upon data processing success.

Segment’s dedication to real-time data handling ensures your insights are based on today’s customer needs and can even help you predict what they want tomorrow. With all of the customer data from various sources unified into a single customer data platform, you can easily see how they behave and inform future marketing campaigns for each individual customer.

Plus, with identity resolution, it doesn’t matter if the data came from an email click, SMS reply, or web browser with cookies disabled. Segment’s AI technology pieces together all the actions of a customer, even from different logins and devices, to get that complete picture.

And while Segment has plug-and-play support for over 400 integrations, it works with newer technologies through APIs. These ensure you don't miss an opportunity to use your data how you see fit, even if there isn't larger app store support for your chosen data tool.