Data Integration: The Key to Business Intelligence

Data Integration: The Key to Business Intelligence

No matter what industry your business is in, advanced cloud technologies allow you to collect a vast amount of data. By 2025, IDC estimates the amount of digital data generated will grow to 175 zettabytes of data worldwide, with 49% of stored data residing in public cloud environments. Your success at translating this data into valuable business intelligence depends on a well-executed strategy focused on data integration.

While this is not an easy task, collecting data without a comprehensive data strategy is almost the same as collecting no data at all.

Common Data Management Challenges

  • Unclear business objectives and KPIs
    Your data can help you achieve your goals, but without a clear purpose of what you’re working toward, it will only take you so far. Communicating objectives and KPIs across your business and technical teams helps you align on priorities. Once you agree on what you’re trying to solve for, you can then build an integrated data strategy that works toward the solution.

    Miscommunication about the goals for data collection occurs frequently between business departments and IT. For example, imagine your company has just developed a new mobile app and Marketing is interested in tracking the most popular screen to determine user engagement. When that request lands in the IT department, that team may interpret the request from a tactical viewpoint and capture data on which screen received the most clicks during a given period. However, digging deeper into this question from a strategic business lens, it would be more helpful to evaluate engagement by capturing data on which screen users spent the most time on.

    Recommendation: Start with a mutual understanding of business objectives prior to gathering data to save time and frustration.
  • Data silos
    Though organizations are gathering increasing volumes of data, it is often separated into silos by business units (Finance, IT, Marketing, Operations, Legal) that have different lenses for determining what data should be stored and analyzed. These data silos can lead to operational inefficiencies, redundancies, critical errors, and unnecessary cost, turning data management into a cumbersome process – with increased risk to your business.

    Recommendation: Establish a single source of truth (such as a Data Lake) and clearly define proper data governance in the early stages of developing your integrated data strategy.

  • Disorganized and uncategorized data
    Another common issue is gathering data without utilizing proper automated data transformation processes or ETL (Extract, Transform and Load) scripts aligned with your business drivers and objectives. This results in disorganized, uncategorized data and unnecessary costs, leaving different departments unable to track and monitor data – or simply in the dark as to what data is available to them.

    For example, companies often spend hundreds of thousands of extra dollars for unused on-demand instances simply because they aren’t monitoring them. This might occur when your development team builds a proof of concept for a small process, spins up several servers, and simply fails to spin them down after testing. Due to a lack of visibility into cloud spend data and poor communication between teams, you will still be spending money on those servers even though you are not using them.

    Recommendation: Leverage data automation and governance services from Amazon Web Services (AWS) such as Amazon Config to automate tagging and improve resource monitoring. This is also critical for categorizing data that has a regulatory compliance impact (GDPR or PCI for example).

Advantages of an integrated data strategy

Overcoming these challenges and ensuring data integration requires a thoughtful approach to data management. As the MIT Center for Information Systems Research notes, a successful data strategy builds a foundation — “a central, integrated concept that articulates how data will enable and inspire business strategy.”

As a partner, we can help you develop a plan that ensures you’re collecting the right data in the right manner to access the most valuable insights for powering your business.

This alignment has several key advantages:

  • Make better business decisions: If you’re not tracking data and gaining real insights — not just what people say is happening but analyzing the actual numbers driving outcomes — you cannot make truly informed business decisions. Going from merely having data to having a plan for using that data as business intelligence provides you visibility into aspects of your organization that you may not have had before.
  • Reduce your risk: Integrating business logic, tagging, and automating data transformation processes improves accountability across teams, reducing the potential for inadequate security or inconsistent processes. A lack of data organization could lead to incorrect or duplicate data with potentially serious ramifications.

  • Meet regulatory governance and compliance: A data strategy reinforces your governance, risk management, and compliance efforts by introducing accountability requirements within your teams. As the Data Governance Institute asserts: “establishing appropriate checks-and-balances that can guide management efforts is probably the single most important role of Data Governance.”
  • Increase your ability to predict, respond, and adapt to unforeseen changes: Though AI and machine learning are rapidly transforming our ability to respond to economic disruption and other factors, you still need to inform learning models and provide proper business logic to leverage those technologies properly. This is particularly true with predictive analytics using a machine learning service like Amazon Forecast for more accurate forecasts.

  • Mitigate seasonality and economic factors: The COVID-19 pandemic is a stark example of an event that shifted spend and focus across business units, creating an enormous change to operations. AI/ML based data science can help you navigate unpredictability and analyze complex variables to determine seasonal trends. These highly accurate tools allow you to plan and adapt accordingly.

  • Gain competitive advantage: The ability to leverage data for business intelligence and predictive analytics provides organizations in every industry one of today’s biggest competitive advantages – especially in a dynamic, quickly changing marketplace. Beyond mitigating risk and unforeseen factors that could adversely affect your business, you can also identify new growth opportunities for your products or services.

An integrated data strategy delivers valuable business intelligence for your organization, offering a roadmap for success both in the short- and long-term. Though you may think about tackling it on your own, it’s a lot easier to navigate with someone who’s been there before and can access advanced AWS services to unlock your valuable data — the right way.

Learn how you can get started.

Zach Shapiro is a Solutions Architect at Effectual, Inc. 

Amazon Web Service as a Data Lake

Amazon Web Service as a Data Lake

“Cloud,” “Machine Learning,” “Serverless,” “DevOps,” – technical terms utilized as buzzwords by marketing to get people excited, interested, and invested in the world of cloud architecture.

And now we have a new one – “Data Lake.” So, what is it? Why do we care? And how are lakes better than rivers and oceans? For one, it might be harder to get swept away by the current in a lake (literally, not metaphorically).

A Data Lake is a place where data is stored regardless of type – structured or unstructured. That data can then have analytics or queries ran against them. An allegory to a data lake is the internet itself. The internet, by design, is a bunch of servers labeled by IP addresses for them to communicate with each other. Search Engine web crawlers visit websites associated with these servers, accumulating data that can then be analyzed with complex algorithms. The results allow a person to type in a few words into a Search Engine and receive the most relatable information. This type of indiscriminate data accumulation and the presentation of context-relatable results is the goal of data lake utilization.

However, for anyone who wants to manage and present data in such a manner, they first need a data store to create their data lake. A prime example of such a store is Amazon S3 (Simple Storage Service) where documents, images, files, and other objects are stored indiscriminately. Have logs from servers and services from your cloud environments? Dump them here. Do you have documentation that is related to one subject, but is in different formats? Place them in S3. The file type does not really matter for a data lake.

ElasticSearch can load data from S3, indexing your data through algorithms you define and providing ways to read and access that data with your own queries. It is a service designed to provide customers with search capability without the need to build your own searching algorithms.

Athena is a “serverless interactive query service.” What does this mean? It means I can load countless CSVs into S3 buckets and have Athena return queried data as a data table output. Think database queries without the database server. Practically, you would need to implement cost management techniques (such as data partitioning) to limit the ingestion costs per query as you are charged on the amount of data read in a query.

Macie is an AWS service that ingests logs and content from all over AWS and analyzes that data for security risks. From personal identity information in S3 buckets to high-risk IAM Users, Macie is an example of what types of analysis and visualization you can do when you have a data lake.

These are just some examples of how to augment your data in the cloud. S3, by itself, is already a data lake – ‘infinite’, unorganized, and unstructured data storage. And the service already is hooked into numerous other AWS services. Data lake is here to stay and is a mere stepping stone to utilizing the full suite of technologies available now and in the future. Start with S3, add your data files, and use Lambda, ElasticSearch, Athena, and traditional web pages to display the results of those services. No servers, no OS configurations or security concerns; just development of queries, lambda functions, API calls, and data presentation – serverless.

Our team is building and managing data lakes and the associated capabilities for multiple organizations and can help yours as well. Reach out to our team at for some initial discovery.