Analytics and AI

Discovery Workshop

The LRS Discovery Workshop engagement is a proven methodology that lets you focus on your most complex business and technology challenges.  Each workshop is customized to your specific business problem and your environment. 

Carried out by members of the LRS Analytics team who have over 20 years of experience architecting and implementing complex analytical solutions across all industries, the Discovery Workshop brings a consultative approach and helps you gain executive approval, alignment of IT and Business teams, and a clearly defined economic impact of solving your business problem through improved analytics and AI. 

A typical Discovery Workshop consists of the following:

• Exploration: Understanding your present and long-term goals, with a focus on industry best practices and technology trends.
• Discovery: Through a series of interviews with key stakeholders, we evaluate your analytical capabilities and business processes to understand opportunities for improvement.
• Recommendations: The roadmap deliverable includes, but is not limited to, an assessment of your current state, recommendations, a high-level solution roadmap, and a business justification.  This is done with a focus on a specific challenge, but the recommendations also consider future demands. 

Modernize Your Data Architecture

There is no shortage of data in your organization.  Applications, edge devices, web logs, and sensors all generate data that can be used to help you make better decisions.  Don’t forget about the value locked up in departmental data silos, spreadsheets, hand-written records, and public third-party sources.

With traditional data architectures, all this information is trapped in databases that don’t talk to each other and are difficult to access.  Poor data availability slows the democratization of insights and prevents you from deploying AI applications throughout your organization.  It also results in shadow IT with unmonitored use of corporate data assets.  Your data must be available to any person with the appropriate permissions from any department in your company.  

In order to stay competitive, businesses need a modern data architecture that based on the following concepts:

• Hybrid or cloud-based
• Virtualization (not federation)
• Providing a single view of all of your data and eliminating silos
• Leveraging the latest Artificial Intelligence and Machine Learning technologies to auto-discover and prepare all of your data

Data Fabric

A modern data architecture lets you analyze the data across all of your sources and multi-cloud, multi-vendor environments.  A data fabric decreases the time to insight while reducing risk and cost:

• Leverage 100% of your data without moving it: spend more time exploring data for insights instead of searching for data.
• Automate data engineering tasks: optimize the delivery of trusted data to support real-time analytics and AI applications.
• Compliance with governance regulations:  Apply policies and enforce standards across all of your data to enable self-service data consumption and collaboration.

Once you understand where all of your data is and can access it, you need a process to catalog, classify, prepare, and govern data so that it can be trusted for use in AI applications.

Many of the governance controls in place today are meant to reduce risk to your company by locking down user access to data.  This places business users who need insights at the mercy of IT for the creation of data sets for analytics.  With most companies being siloed in nature, there is no standard view of data lineage and quality across the enterprise, which slows the creation of trusted data for AI applications.

Using a DataOps approach, we help you make your data business-ready for AI.  Tenets of this approach include:

• Intelligent Data Catalogs: Lets users of all types browse for data in all sources while leveraging a policy engine that controls access to those assets.  Automated, dynamic masking of sensitive data provides maximum access while reducing compliance risk.  The catalog metadata also shows relationships between data, AI models, and other assets.
• Automatic Classification of Assets: AI is used to auto-classify your data in cloud, on-prem, and hybrid sources.  Sensitive and personal information is located and marked, and security policies manage access to any confidential information.

• AI-Driven Integration: Automate the mundane tasks of data engineers to prepare data and auto-create data pipelines across distributed data sources.

• Governance Across All Data Sources: Get an enterprise view of data lineage and quality to ensure the transparent deployment of trusted, ethical AI models.
• Operationalized Data Quality: Assign quality benchmarks and measure them continually so users and data scientists have a window into the quality of their data.

How long did it take you get your last report or dashboard?  By the time the dashboard was delivered, was your business question even relevant anymore?

Your data is fast.  The platform you use to discover business insights needs to keep up. 

Cloud-native Augmented Analytics running against your scalable cloud data warehouse is the answer and can be used in the following areas:

• Augmented Data Preparation: AI and ML can be used to auto-discover and prepare data for analysis and AI models.  Click here (link to Catalogs, Integration, & Governance page) for details how we can help you with DataOps and automated data pipelines.
• Augmented Business Intelligence:  Lets executives, citizen data analysts, and power users search, build dashboards, and ask follow up questions instantly.  Explore your data with Natural Language Query (NLQ) by typing your questions and let the Natural Language Generation (NLG) in the platform explain key insights to you or point out important facts you may not have detected. 
• Augmented Data Science:  Use AI to build AI.  Automated data preparation, feature engineering, hyperparameter optimization, and ensembling lets citizen data scientists create and select the best AI models for their use case.  Automatic drift and bias detection ensures the models are explainable and ethical.