In today’s world, we’re awash with a deluge of data - constantly being generated from the devices we use, the systems we build, and the interactions we have.
Organizations across every industry are using this data to fuel digital transformation and gain a competitive advantage. Pair this with the emergence of cutting age technology like generative AI - data, and becoming Data-Driven is more important than ever. And yet, this data is often stratified, disconnected, siloed, and difficult to use - meaning most organized are leaving a highly strategic asset on the shelf.
It requires new end-to-end tools and a proven approach to activate this data in a way that generate tangible value for the business. That’s why leveraging a human-centered analytics product like Microsoft Fabric, backed by the proven strategies and tactics used across dozens of clients at Interloop is imperative to capturing this opportunity.
Struggling to deal with the complex data environment? You’re not alone.
Organizations are facing an array of data challenges today such as:
Scaling Data & Analytics across the organization while reducing costs & optimizing existing data and management
Gaining Business Intelligence Adoption to streamline data usage and insights across departments
Encouraging data literacy by making data more accessible and easier to understand by both technical and non-technical team members
Balancing the need for governed and self-service data exploration and analytics
Limited scalability of legacy solutions while demand from the business explodes
Breaking down data siloes across the various departments and functions of the business
Delivering on the promise of automated insights with limited resources
Like most complex challenges, investing in People, Process, & technology through training, tooling, and a proven approach is essentials to avoiding failed data initiatives.
Best of Breed vs All-In-One, which approach is best for my organization?
If you search for “Modern Data Platforms”, often times the first result will include an architecture diagram that looks something like this:
Building, maintaining, and encouraging adoption of a Modern Data Platform can be a steep learning curve for most organizations but those that are able to overcome this challenge are likely to benefit greatly.
If you break it down, there are several key components that make up a modern data platform approach.
Connection & Extraction
In order to analyze your data, you need to be able to connect to the various cloud applications, databases, file storage, and streaming data sources that generate the data. A modern data platform needs to manage the access credentials and keep open live connections to these data systems.
From there, it needs to be able to extract the data into a centralized location - often called the data lake. While some approaches such as Data Virtualization or Zero ETL work to remove any copying of data, in practice - we’ve found that landing a raw copy of the data allows for the most downstream use cases.
Example best of breed tools include: CData, Fivetran, Funnel.io, etc
Organization & Modeling
Once the data has been landed into the Lakehouse, there is work to be done to standardize, organize, and model the data.
Example Tools Include: DBT, Transform, Data Bricks, etc
Consumptions & Activation
Ultimately, being able to make better decisions faster is the key outcome of leveraging
Example Tools Include: PowerBI, Tableau, High Touch, Census
Governance
While entropy, or the idea that all things fall into chaos if not maintained - is just as relevant in Data as it is in physics. Organizations must work to maintain, document, and promote datasets in order to drive adoptions across the organizations.
Companies will also need to be very mindful of the contents of this data and the sensitivity of the data. This often means auditing for PII (Personally Identifiable Information) and ensuring that the stricture of data privacy laws such as GDPR & CCPA are being upheld.
The MAD (Machine Learning, AI, & Data) Landscape in 2023
An average modern data platform consists of 8 or more tools that must work in harmony to create the desired outcome. For many organizations, this means 8 different contracts with different payment terms, limitations, and usage. Combine this with the need to train your team on the nuances of each tool, the modern data platform approach, and the data systems and processes of your organization - this can often become overwhelming.
Example Tools Include: Secoda,
To view a full list of tools available in the MAD (Machine Learning, AI, & Data) Landscape - check out this great visual by Matt Turck - https://mattturck.com/mad2023/
Introducing Microsoft Fabric, a unified SaaS-based solution for data & analytics
Microsoft Fabric combines several of Microsoft’s flagship data products such as Data Factory, Synapse Analytics, Data Explorer, and Power BI into a single, unified experience, on the cloud.
The fabric platform is a cost-effective and performance-optimized fabric for business intelligence, machine learning, and AI workloads at any scale. It is the foundation for migrating and modernizing existing analytics solutions, whether this be data appliances or traditional data warehouses.
By establishing connectivity and integration, organizations can transform their unstructured and siloed data into a valuable strategic asset through:
Data modernization backed by the Microsoft Azure Cloud
Cloud-native applications at any scale
Responsible, powerful AI to make more informed decision-making
Analytics and insights at a faster rate
Responsible for machine learning and artificial intelligence
Governance backed by Microsoft Purview
As we all know, powerful tools don’t solve organizational challenges on their own. It takes a strategy, expertise, and the ability to execute in order to harness the value of any platform.
That’s where Interloop comes in - we bring our proven approach, deep expertise, and a team of capable data engineers, data analysts, data scientists, and delivery managers to ensure you achieve a successful data initiative.
Solving complex data and analytics challenges with Fabric & Interloop
Data is inherently agnostic but can be used in distinct ways based on your business function & use case. Here are a few examples of how you can use data to optimize your organization.
Marketing
Improve Campaign Analysis & Planning
Optimize Paid Media spend & activation
Perform unified website, social, & email analytics
Sales
Identify better opportunities for upsell & cross sell
Develop enhanced quoting & pricing plans
Improve sales performance through analysis
Operations
Optimize Production & Delivery
Enhance Inventory Planning & Performance
Streamline fullfillment & distribution
People
Reduce employee turnover & improve retention
Gain visibility into recruiting & performance
Monitor benefits, rewards, and compensation
The art of the possible
Your customers, employees, partners, and suppliers likely have unmet needs that could be uniquely solved by combining your business strategy with unified data, comprehensive insights, and faster decision-making.
Ready to begin your journey, let us loop you in by having a conversation with an expert today.