Leveraging Business Intelligence to enable Machine Learning.

Data Analytics & Machine Learning (ML)

Let our Team of Data Science Consultants help empower your organization in implementing sophisticated Machine Learning models along with a unique AI strategy that fits your long-term business data initiatives.

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How Machine Learning Can Impact Your Business

The list of benefits is long. Organizations can optimize operations, shed manual processes, and move faster. In fact, Forbes Magazine stated that 76% of enterprises prioritize AI and machine learning over their other IT initiatives.

Decision Making

The goal of AI has always been to generate smarter decision making. It’s not that we’re not able to think critically as humans, we’re just limited in how quickly we can process and coordinate mountains of data.

Reducing Errors

Once the foundation of your AI and automation models are established, you’ll notice manual errors starting to disappear.

Tackle Complex Problems

Introducing deep learning and machine learning into business strategy allows you to take on more complex problems.

Automation

The most common output of AI, there’s not a business process that automation can’t positively impact.

Increase Operational Efficiencies

Between automating your most repetitive tasks and expanding your operations with AI, your business will see an immediate increase in efficiencies.

Improved Customer Experiences

Using deep learning and NPL, it’s never been easier to provide timely, tailored experiences for customers.

We ARE Data Science Experts

We help our clients develop a data-driven culture with insights from their boardroom to the shop floor that delivers faster and easier access to their data and builds intelligence into every process.

Planning

Here we’ll set the foundation for operational excellence. Working together, we’ll develop a strategy to deploy the appropriate AI and Data Science solutions.

Modeling

With our plan in place, our team of consultants will help build, optimize and deploy the solutions. From the most simple forecasts to complex systems – we can make your vision a reality!

Infrastructure

As a Microsoft Solutions Partner, we’re experts in leveraging the Azure Cloud to build scalable solutions capable of growing along with your Data and Machine Learning (ML) needs.

Many organizations travel the data and analytics path to create the technology capabilities, applications, and processes used to collect, store, and analyze data to support business decision making. These organizations are aware of the sheer lift that is involved with setting up the infrastructure, acquiring and retaining personnel, and developing a culture that not only creates data and analytics, but also sustains and develops it.

Most professionals in data and analytics do not view themselves as capable in the domain of Machine Learning. There is a stark line, real or imagined, between Business Intelligence (BI) and Machine Learning (ML). However, advances in machine learning have led to the blurring of this false distinction between BI and ML.

Understanding an organization’s data and analytics capabilities requires an evaluation of where the organization falls on the continuum of a data and analytics evolutionary model (below).

Data & Machine Learning Maturity Model

click below to learn more about your own Data & ML maturity level.

Little to no formal analytics, ad-hoc analytics, no or under developed strategic plan, limited human resources, relies on intuition rather than data driven insights.

Current State: Anti-Analytics

Technology Platform: On premises. Desktop machines, MS Office tools (e.g. Excel).

Data Practices: Little to no data movement, little to no formal processes. Waterfall, if anything.

Competitive Orientation: Laggard; not using data

Questions Asked of Data: None to retrospective – what happened in the past?

Often, this is what people refer to as “Business intelligence”. Treated as reports or dashboards. Data visualization becomes common.

Current State: Traditional analytics

Technology Platform: On premises. On-prem server/storage, networked technology. Limited cloud presence. Early hybrid. PowerBI or Tableau. SQL reports. MS Access.

Data Practices: Extract, Transform, Load, often informal. Little to no practices to maintain data. Waterfall method.

Competitive Orientation: Market equilibrium

Questions Asked of Data: Retrospective to contemporary – what happened in the past and what is happening now?

Pattern detection becomes quantified. Inferential modeling initiates so organizations can start to understand why. Complex model building. Correlation and regression start to be used.

Current State: Traditional analytics

Technology Platform: Shifting to a hybrid model. Stand-alone software tools (e.g. Python Notebook, R-Studio, SAS, SPSS, MatLab). No deployment.

Data Practices: Extract, Transform, Load. DataOps initiated. Waterfall transitioning to agile.

Competitive Orientation: Competitive advantage initiated.

Questions Asked of Data: Retrospective and Contemporary – what is happening now and in the immediate future? Why is it happening?

Organization can make specific, quantitative statements about the future. They can predict needed stock levels, staffing levels, product demand, breakage, etc. Advanced regression, multi-level modeling, supervised and unsupervised learning, classification and clustering.

Current State: Advanced analytics

Technology Platform: Heavier cloud presence. Moving away from hybrid to a cloud-only model. Data lakes, machine learning services in place. Endpoints developed. Automated deployment.

Data Practices: Extract, Load, Transform. DataOps fully operational. Agile/recursive method.

Competitive Orientation: Competitive advantage realized

Questions Asked of Data: Retro, contemporary, and future – What will happen? When will it happen? Why will it happen?

Deep-learning algorithms, advanced object detection, custom NLP solutions in place and working. Well developed strategic planning looking 3-5 years ahead.

Current State: Advanced analytics

Technology Platform: Full cloud presence with limited on-premises technology. Fully automated deploys.

Data Practices: Extract, Load, Transform. DataOps fully operational. Agile/recursive method.

Competitive Orientation: Maximized competitive advantage

Questions Asked of Data: Adaptive Future – Machines making decisions based on what, when, and why future events happen.

If you’d like to learn more about our capabilities or explore how we can assist in your next Data or Machine Learning initiatives – please leave our Team a note below.