Companies understand their data is a strategic corporate asset and they want to utilize it to make sharper, speedier decisions; but the problem is it is complicated. Often data is tossed in silos, trapped in individual departmental systems, the quality of the data is inferior, and it is wrapped in unreliable manual processes. To maximize the value

of the company’s data it is imperative for an organization to create, process and deliver information in an optimized way. The three key parts to developing a strong enterprise data and business intelligence strategy are data architecture, integration, and visualization.

Data Architecture

Data architecture is the process of standardizing how organizations collect, store, transform, distribute, and use data. It also is the roadmap that defines the strategy that guides integration of data assets. The objective is to deliver relevant data to individuals who need it, when they need it, and how they need it. It is important to remember that data architecture is as much a business decision as it is a technical one. Data Architecture can be broken down into the following elements:

  • Outcomes: Models, definitions, and data flows that achieve results.
  • Activities: Forms, deploys, and procedures that achieve objectives.
  • Behaviors: Collaboration, mindsets, and skills that achieve success.

A key aspect of data architecture is the challenge to create a single repository that represents a “source of truth” by which user can access for critical business data. This eliminates the costly and time-consuming activities that occur when businesses reply on multiple sources and manual aggregation. Another important factor is whether to choose a traditional on-premise solution, cloud-managed solution, or a hybrid of both. This decision should be thoroughly planned, and business requirements carefully considered.

Since data architecture is the data blueprint that will unlock endless business value.  It should be the foundation for standardizing data collection and usage across the enterprise, giving all data users access to valid, pertinent data promptly and cost effectively. Data architecture bridges the gap between business leaders and IT, giving them a platform that allows for flexibility and responsiveness, while ensuring that technology and business strategy align to power the business forward.

Data Integration

Data integration involves combining data from several disparate sources, which are stored using various technologies, and upon completion of the project, provides a unified view of the data. Data may also be accessed through a federated service, which accesses distributed, heterogeneous systems at the time of query, rather than pre-processing and storing data in one central place. Simply put, data integration is the essential link between information and insight. Businesses that ensure their various databases can “talk” to one another are able to take advantage of the details they’re collecting.

Data integration into a centralized data warehouse becomes more important as application environments trend towards microservice architectures. As applications grow and build complexity over time, the ability to react to business changes and add new features becomes extremely difficult to manage without affecting other parts of the system. As a result, IT development teams are breaking these monolithic applications up into smaller easy to manage services that can scale more effectively. This process is what led to the name microservices.

Other IT organizations may have their applications split across business lines in a federated architecture, resulting in systems that are distributed and heterogeneous. They also may contain overlapping datasets for entities like customers and products. It becomes the job of the data integration team to bring these disparate sources together and align them across business processes. Conflicts between systems can result in poor data quality and require data stewards to define which system is the master dataset and business rules for managing and processing that data.

Traditionally, data warehouse architectures used an Extract, Transform, Load (ETL) process. This process worked like this:

  • E – Receive the data and extract it
  • T – Transform the data in flight
  • L – Load it into a system.

Newer architectures are choosing a slightly different approach called ELT. This process works like this:

  • EL – Receive the data and load it as is.
  • T – Transform it on the database you load it to or transform it via front-end analytics tools/visualization tools.

The benefits are greater speed of extraction and loading, and you capture all the data, whether you need it today.

Every organization that produces reports or dashboards often has some flavor of data integration platform working behind the scenes to pull data from source databases, transform data, and load data into a dedicated data repository for BI and analytics.

Data Visualization

Data visualization is the way we present data in order to make data driven decisions. Visualizations now have more power for the end user to analyze, build, and integrate machine learning inputs into visualizations. It is the key to self-service business analytics where people in the business can access data and build visuals for specific problems. To be successful in data visualization, it’s important to know your objective, know your audience, highlight key findings, eliminate fluff and simplify.

New visualization tools make it easier for everyone to explore data on their own. These self-service tools allow users to access data from anywhere in the world–allowing executives to check reports and make business decisions from anywhere. Today it is possible to build reports that are interactive and contain data that is in real time and is automatically refreshed.

A traditional approach to visualizations is to build a report or dashboard and deploy it for others to see. New capabilities are arising for the data visualizations to be built in a way that they can be embedded in other solutions through APIs and other means.

Every month, new updates and enhancements enable users to create much better, profound and creative ways to visualize data. But

the way we present data is more than just beautiful, captivating pie charts and bar graphs. We must convey a message to the audience effectively to enable quick, confident data driven decisions. This process of translating data analytics into a narrative that influences a business decision is called data storytelling.

Becoming a data storyteller will be even more important as we travel into the future. A new breed of data tools is making it easier for people in all business functions to access and explore captured data on their own, with the result being a greater number of data insights being shared and translated within companies than in the past. The communication of these insights will continually need to be improved so that the largest amount of value can be extracted from these insights; otherwise, no changes or growth will happen, since there can’t be action without understanding.

The latest visualization platforms allow users to pick from numerous visual display options to best convey the message to the targeted viewers. As businesses improve their data communication through better visualizations, they will convey their messages in a more compelling way. Columns and rows of numbers in a spreadsheet cannot compete with the ease of comprehension and powerful storytelling accomplished through visualizations.

What is Your Data Telling You?

Collecting and aggregating data for your business holds a lot of potential value, but until you can uncover the insights it provides, it will not tell much of a story. It needs to be understood and processed. The true value comes when it is communicated to your team and translated into actions and outcomes.

Building a strong data strategy will help.

you ensure greater consensus around data priorities, standardize of data processes, drive greater collaboration, and, as a result, bring unprecedented insights to your company. In addition to serving as a driver to greater data quality, the benefits of building a comprehensive data strategy include:

  • Driving technology roadmaps
  • Directing sales efforts
  • Improving customer experiences
  • Standardizing tools and access

Building a data strategy might seem overwhelming at first thought, but with the right approach, your solution has the potential to become a game changer for your entire organization. If you are interested in harnessing and understanding your data; while finding the story it tells, LBMC Data Insights can help.

Transforming your data into information, and information into business impacting insights is no easy task. LBMC Data Insights can provide you with the methodologies and tools to leverage your data to drive revenue.