Data & Analytics Insights

A regional Healthcare Payer was having trouble determining accurate calculations for their Healthcare Effectiveness Data & Information Sets (HEDIS) metrics.

An eCommerce retailer was plagued by a fractured data ecosystem making it impossible to have a 360-degree view of their customers.

An energy solutions organization was working with an antiquated data foundation. An upcoming project required the accelerated adoption of modern data solutions and time was of the essence.

A global retailer has disparate ERP systems and obsolete data models leading to siloed data. These pain points are draining resources and causing customer service setbacks.

An e-commerce fulfillment organization was in serious need of a modern data visualization tool to gain valuable insights into their operation. Heavy reliance on static reports left both IT and business groups unhappy.

Care managers at a healthcare payer organization are suffering from siloed data effecting data quality and hampering capabilities to make real time decisions creating risk for both members and the organization.

A healthcare payer organization needed to pivot from data sources delivering stale data to sources that provide more quality information needed for HEDIS reporting.

A regional healthcare payer’s data was severely siloed forcing data science and advanced analytics teams to spend more time on data wrangling than data analysis.

A growing health insurance entity was hampered with data silos and extensive data sets making it increasingly laborious for stakeholders to identify areas of opportunity for care gap closures.

A growing healthcare payer organization is suffering from lagging data processing times that is adversely affecting the organizations HEDIS program.

The governance team of one of the nation’s largest bank-based financial services companies was badly siloed in its analytics capabilities. As it stood, the team members had no internal resources to design and deploy business intelligence tools.

Our client is one of the leading dental services organizations in the country supporting over 240 dental practices and businesses. Prior to our relationship, the client was utilizing SQL Server Reporting Services (SSRS) software for its analytics and reporting needs.

A leading eCommerce retail supplier needed help centralizing their enterprise data. Multiple disparate ERP systems were being used, duplicating the amount of work needed to input customer information fivefold.

The Data Ideology team guided the client in defining the optimal alignment of their IT resources to principal functions in enterprise data management.

Data Ideology provided a comprehensive analysis of key metrics and business processes that could help the client identify high priority areas where centralized data solutions would be needed first.

The Data Ideology team led an in-depth discovery session with the various business stakeholders to understand their future-state systems and reporting requirements.

The client had a hard deadline to build and deploy an external reporting application via Tableau server to deliver timely, actionable insights and system-wide visibility of data to clients, both historical and real-time reporting.

Our client is amongst the fastest growing healthcare payer organizations in the nation. However, like many of the clients we help, they were suffering from slow data processes due to legacy systems and software.

The marketing team at a prominent health insurance company had no means to consolidate member contact preferences due to siloed data. This negatively affected communication between this group and their 1+ million members.

Leveraging the shared datasets feature from Power BI allows your company’s analysts to work from a single source of truth.

Do your business users have trouble finding reports and navigating through the Power BI Service? If your organization uses Microsoft Teams, you can embed a report directly in a channel for greater visibility.

Capabilities to bring together self-service analytics and enterprise analytics solutions.

Many Power BI users don’t realize that there are actually two methods of getting Desktop on your PC.

Ever wonder how often your Power BI report is being viewed? Set up a Usage Metrics report to monitor who’s utilizing your report.

Struggling to get business users in sync about how to slice and dice a metric in a report?

Most BI departments utilize DEV, TEST, and PROD environments to release reports to the business. Here’s how to accomplish that in Power BI.

A common mistake for new Power BI users is not knowing the difference between reports and dashboards.

Deciding upon and getting the appropriate tools is only the first step of the journey to a successful adoption.

You bought Power BI. Now what? Several organizations that we’ve spoken to focus their time and efforts on developing Power BI assets and content.  They seem to only think about their deployment and governance approach as an afterthought. This approach leads to a number of unintended consequences that ultimately impact the organizations adoption and value…

By implementing Snowflake’s best practices, it becomes possible to better understand and articulate your data warehouse’s overall structure and layout.

The Snowflake Data Platform is designed for scale, efficiency, and ease of use. It supports an unlimited number of Virtual Data Warehouse clusters that offer shared access for optimal performance.

With the enormous revenue potential tied to digitization, data management is becoming even more critical, reaching an all-time high in demand for enterprise data management initiatives.

Success with Snowflake will lead to building a data foundation that future initiatives can build on to further advance and grow the organization’s data and analytics capabilities.

Cloud-based technology like Snowflake provide financial services organizations with a much needed competitive advantage.

There are two main data movement processes for the Snowflake data warehouse technology platform. Extract, Transform, and Load (ETL) vs. Extract, Load, and Transform (ELT).

Snowflake’s core architecture is built on a multi-tier cloud data platform that scales independently. Snowflake’s multi-cluster shared data architecture consolidates data warehouses, data marts, and data lakes into a single source of truth that enables any data workload on any cloud with a simple, powerful, and flexible platform.

The Snowflake Cloud Data Platform is one platform to build all your organization’s data apps. The advantages of leveraging Snowflake as your modern cloud data platform differs between the specific use cases of your organization. Here are some more of the critical capabilities that can help transform your organization.

Data Scientists and a traditional scientist have a lot in common.

One platform to build your data apps.

Building a Modern Data Platform with Snowflake can alleviate many organizational challenges.

There is no shortage of data warehouse technology platforms that can serve as the foundation for your organization’s modern data platform.

The Tableau Suite of products consists of several resources that are designed to enable users to integrate various data sources and create visualizations that can be shared enterprise wide and externally. Here’s an overview of each Tableau product.

No matter how beautiful and informative and actionable your Tableau visualizations may be, there will always be a person or use case for exporting data to a tabular format usually via excel.

One of the best practices learnt during my military service and applied to my Business Analyst career was the importance of collaborative outreach and thorough communication with key stakeholders.

A data model enables you to make decisions based on facts instead of educated guesses. However, the value goes far beyond that. With data modeling, you’re easily able to store and access information that will benefit you and your organization.

Integration streamlines the data lifecycle to enable a data-driven organization by moving the data closer to the point of action.

The increasing speed and pace of business contributes to several data challenges.

Organizations realize the tremendous benefits associated with getting data into the hands of everyone who needs it, while moving the data closer to the point of action.

A centralized data warehouse can serve the needs of numerous business units simultaneously.

The increasing speed and pace of business contributes to several data challenges.

Data governance is a challenging topic and directly affects how an organization interacts with its data. Successfully applying a Data Governance strategy requires managing the dynamics of an organization that allows for organizational change.

Many of our customers have grown primarily through mergers and acquisitions to achieve accelerated growth. As a result, the organization quickly faces the prospects of multiple applications and systems that deliver similar functions and store the same data.

How do we gain buy-in with enterprise Data Governance and data quality programs and processes when your organization is exercising caution due to past failures? Data Governance has become a critical discipline and important area of focus for organizations to realize operational efficiency and to support business growth.

Data is at the center of People, Process, and Technology, representing a crucial asset used by all three. Simply put, people should embed data in any project to ensure that organizations follow a defined road map around enterprise data governance; technology can also help enhance and automate several processes and transactions.

To progress to a future state of mature Data Governance and analytical capability, decision-makers should focus on starting with the integration of the data across an entire enterprise. A Data Governance program will be critical to support the continued growth of an organization.

Master Data Management (MDM) allows you to manage data assets better. MDM’s goal is to implement best practices that will help you optimize the management of your organization’s data assets.

Today, many organizations have numerous applications and systems in which data, spanning multiple departments, quickly becomes fragmented, duplicated, and even outdated.

A well-known issue with legacy data platforms is what’s known as the garbage in, garbage out (GIGO) obstacle. As data began to flourish due to cheap storage options and increased digitization, data quality issues and fragmentation proliferated, creating GIGO issues that affected an organization’s business intelligence.

Scrum is a Delivery Framework, under the Agile umbrella, that can accelerate initiatives, breakdown data silos, and bring organizations together as a team to progress any project. Individuals certified in Scrum are known as Scrum Masters.

Technical debt is commonly described as efforts deferred for the purpose of meeting deadlines or goals that will later need to be revisited and reworked for the final state product. It’s a tradeoff between gaining short-term benefits while sacrificing long-term value and overall efficiency.

With increasing globalization and technology developments spurring modern economics, Data Strategy has been vital in identifying and understanding customers.

Data-driven organizations understand that their data is a strategic asset and to properly harness its power it must be supported by a sophisticated data strategy.

Many c-suite executives are perplexed at the amount of money their organizations spend implementing and integrating data and software applications with limited evidence that they will even align with desired business outcomes.

Healthcare Interoperability is a framework where systems are coordinated to provide standardized methods of interacting with patient data.

In recent years, Health Information Exchange (HIE) capabilities have helped mobilize patient data allowing clinicians and healthcare managers to provide near real time diagnosis closing the gap on member care.

In late December of 2020, bi-partisan legislation known as the No Surprises Act was passed by the U.S. government. The main purpose of the bill is to eliminate surprise medical costs for out-of-network services.

One of the best practices learnt during my military service and applied to my Business Analyst career was the importance of collaborative outreach and thorough communication with key stakeholders.

Siloed data hinders collaboration and can cause serious downstream issues affecting multiple departments such as enrollment, claims and reimbursement. This data fragmentation stops the payer’s ability to get a 360° view of their customer, making it difficult to provide a more personalized experience.

In order to implement automation applications and other machine-based processing, data must follow an agreed-upon structure and standardization. The global healthcare community believes they are close to achieving this with Fast Healthcare Interoperability Resources (FHIR).

It is important that payer organizations continue to seek ways of improving their HEDIS scores year-to-year. One certain way of doing this would be to implement a proper data management system.

When it comes to financial services, people’s lives can be somewhat fragmented. Depending on the situation, they might be juggling two or three different financial institutions along with several diverse financial products.

An extremely vital aspect of the banking and finance sector is their ability to manage exposure to losses & risk and to protect the value of its assets.

With the rapid growth of FinTech organizations, the banking and finance sector are chomping at the bit to figure out a way to accelerate their data initiatives.

Just like most industries, banking and finance have forever changed due to the explosion of big data and the infrastructure required for processing such data.

Cloud-based technology like Snowflake provide financial services organizations with a much needed competitive advantage.

At Data Ideology, we have made a concerted effort to recognize and achieve high levels of gender equity in both leadership and employee level positions.

The World Economic Forum recently released the Global Gender Gap Report for 2021. The United States isn’t even in the top 20 for gender equality. Women only make up 14% of cloud computing jobs and 32% of data and AI positions.

Applying for jobs can be challenging in any situation, but the task can be especially stressful for women in IT considering their underrepresentation in technology fields.

If data is the new business currency, then predictive analytics are the means in which organizations can take control of that currency to maximize its benefits.

Overall spending on Artificial Intelligence (AI) systems is projected to reach $79.2 billion in 2022, which is more than double the amount spent in 2019.

According to a recent retailer’s report conducted by Blue Yonder, only 14% of the 300 executives surveyed say their fulfillment locations are fully automated.

Although achieving a 360-degree view of customers isn’t a new concept by any stretch, retail organizations are still struggling to achieve this critical feat from a data management perspective.