Every data leader has experienced this frustrating disconnect.
You invest extensively into both your data infrastructure and analytics capabilities. You establish an adequate pipeline infrastructure. You implement meaningful dashboard solutions. You employ excellent data engineers and business analysts.
But when it is time for any type of business decision, most teams still rely on gut instinct, disjointed reports, and historical metrics to inform their decisions.
Unfortunately, the disconnect between your data pipeline infrastructure and actual business decision-making is becoming wider, not smaller.
For a VP or Head of Data, this is not an issue with your tooling being insufficient; it is actually a systematic failure of the way that modern data architecture has connected your infrastructure and your data to the real-world outcomes.
In this blog, I will explain the reasons for the continued disconnect between your data pipeline infrastructure and actual business decision-making, as well as how to design data structures that can deliver the desired results from today's businesses in a timely manner.
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What Are Data Infrastructure and Analytics?
Data infrastructure and analytics refer to the entire suite of technology and processes an organization must leverage to:
Capture data Transform and process captured data Store captured data in an efficient, long-term manner Analyze captured data for meaningful insights
Components of Data Infrastructure and Analytics
Data ingestion systems Storage technologies (Data Lakes, Data Warehouses, etc.) Transformation pipelines Business Intelligence and reporting technologies Governance and compliance monitoring solutions
What Are Organizational development
Most organizations have growing amounts of data, dashboards, and less clarity regarding what that data means.
Key Takeaway: Data Infrastructure is growing rapidly, while Strategic Decision Making is not.
The Gap: Data Infrastructure vs Strategic Decision-Making Gaps.
The Problem as we see it
The Pipeline Layer
This includes:
- ETL/ELT processes.
- Data ingestion tools Data storage
- The Pipeline is developed well.
The Decision Layer
This includes:
- Business Teams
- Product Decisions
- Strategic Planning
The decision-making decisions are often disconnected.
The Gap
The Gap is a result of:
Data exists that is not considered credible Data insight but they are not shown to be able to produce insights Data is available with no standard or way to compare
As of today, organizations have processed petabytes of data, and continually fail to provide consistent answers to basic business questions.
Key Takeaway: The problem is not an abundance of data. The Problem is Misalignment.
Reasons for the Gap
Despite the emergence of significant advancements in data infrastructure, the gap is continuing to expand.
1. Over-engineering data infrastructure.
Modern data stacks are becoming more complex.
Organizations are deploying:
- Multiple Data Platforms
- Complex Orchestration
- Real-time pipelines to execute business use cases
Before having determined any business use cases
Results
- Increased time to make decisions
- Increased operational overhead
- Increased team confusion
2. Data is not aligned with business needs.
Data teams are focused on:
- The Pipeline and Data flows
- Performance Metrics and Performance of the Data Pipeline
- Scalability of their Data Pipeline
The Data Teams are often isolated from the business teams and involved in the marketing, manufacturing/CPO, sales/profit use of customers.
Business Teams are primarily focused on:
Customer Outcomes
- Performance Metrics (Sales volumes/profit margins)
- Insights derived from performance metrics
- Customer Results (customer retention)
3. Noone owns the data.
There is no clear ownership of:
- Data quality
- Data definition
- Data metrics
Results
Competing reports for the same metric.
Loss of trust in the metrics that will be provided/used to make strategic decisions.
Tool Explosion
The contemporary tech stack consists of:
• A bunch of ingestion engines
• A bunch of storage (aka data warehouse)
• A bunch of BI tools
What Twists All this Up:
• Data Silos (aka disparate data sets, they don’t encompass the whole view of things)
• Complexity (aka cumbersome integrations)
5. Insight Delays
A lot of pipelines running today in real-time processes are still running in batch processing mode.
What Twists All this Up:
• Making decisions based on old data.
• All of this drives complexity on a faster rate than usability.
What’s Causing the Problem? Architecture versus Results
Whenever organizations are creating their architecture there are three items typically considered:
• Scalability
• Flexibility
• Availability/Performance
But no one looks at:
• Decision Making
• Clarity
• Confidence
What’s Being Missed?
The straight line between:
• Data → Insight → Decision → Action
Why Is This Important?
Without this connection, the data instead becomes just another tool for reporting; it doesn’t become an engine for decision making.
In Summary, Architecture must be driven by results instead of just by infrastructure.
What Are the Core Elements of a Modern Data Architecture?
To conform and adjust as business methodologies evolve, we need to think about architecture differently.
1. Data Ingestion Layer: Ensure reliable pipeline to load:
• Completeness
• Quality
2. Storage Layer:
• Data Lakes (raw data)
• Warehouses (structured analytical data)
3. Transformation Layer: Standardization of data loads before utilisation.
4. Analytics Layer: Provide:
• Dashboards
• Reports
• Ad-hoc analysis
5. Decision Layer (Most Times Is Non-Existent):
• Business Logic
• Metrics Definitions
• Decision Workflow
What Are the Core Components Of A Cloud Based Data Platform?
A successful cloud-based, modern environment will be equipped with:
1. A Unified Data Model: Single source for measuring metrics.
2. Real-Time Capability: Fast determination of insights.
3. Governance Model: Ensures degree of metric consistency and confidence.
4. VisibilityScalability means that you are increasing the capability of the platform to manage more data.
(Overview) A quality platform is built on the basis of usability rather than its ability or capacity to manage growth.
Cloud Platforms Delivering Comprehensive Solutions
Companies use one or more cloud suppliers to run their operations.
Amazon Web Services
Solid Infrastructure
Microsoft Azure
Great enterprise connectivity
Google Cloud Platform
Intelligence analysis and machine learning features
How To Choose A Cloud Platform
Some considerations for selecting a cloud supplier are:
Current capabilities Amount of data How knowledgeable your team is
(Overview) The architecture of the platform does not greatly affect your choice of platform when compared to the project management approach.
Side-by-Side Comparison of Leading Managed Services for Data Warehouse Solutions
Utilizing a managed service as your data warehouse will allow for simplified management of data warehouses; however, managed service platforms can be used successfully depending upon how you utilize the platform.
Pros of Managed Services
Lower cost of operations Quickly deliver service compared to internal development
Cons of Managed Services
Vendor lock-in Restricted customization options
When Should You Use Managed Services?
Small- and mid-sized organizations Growth-oriented organizations
(Overview) While you can speed up your data delivery process using managed services, you need to have someone to oversee the success of the process.
Best Practices for Setting Up Real-Time Data Pipelines
Real-time systems decrease the time it takes to understand how your business operates.
Best Practices
Use an event-driven architecture Incrementally process your data Calculate the latency of your events in real-time
Why Is This Important?
Decision making is faster Responsive business
(Overview) Real-time data is only worthwhile if you are using that data to make real-time decisions.
Best Ways To Use The Software To Add Data
Choosing the right tool for your business is critical to your success.
3 Primary Characteristics To Look For In A Good Tool
Scalability Reliability Ease of integration
Types of Common Data Tools
Streaming tools ETL/ELT tools Data integration tools
(Overview) Tools for adding data should streamline processes, not create more processes.
Choosing A Data Lake Solution For Enterprise Analysis
For high-volume data centers, data lakes are critical in an enterprise environment.Factors to consider:
- Cost of storing data
- Capacity of processing
- Ability to integrate with analytical tools
When are the Best Uses of a Data Lake?
- Store raw data
- Create machine learning pipelines
Data Lakes can support scaling but need strong governance.
How to Close the Gap Between Pipelines and Decisions.
1) Create a Decision-Focused Architecture.
a. Begin with your business questions. b. Identify key metrics you want to measure. c. Design your associated infrastructure.
2) Standardize Your Metrics.
a. Create unified definitions of your metrics. b. Create a shared data model for your metrics.
3) Embed Your Analytics into Your Workflows.
a. Do not create dashboards; instead, integrate analytic insight directly into:
- Product Experiences
- Operational Systems
4) Improve Accessibility of Your Data.
Make it easy for people who are not data experts to:
a. Access Your Data.
b. Understand Metrics.
5) Create Data Ownership.
Assign accountability for:
a. Data Quality. b. Analytics Metrics.
6) Focus on Actionable Insights
a. Only use insight that will lead to making better decisions — Do not create reports.
The goal is not to create better dashboards, it’s to create better decisions.
Case Example – How to Close the Gap.
Scenario:
A Software as a Service (SaaS) Company has:
- many advanced pipelines, and,
- many dashboards, but struggles with making quick decisions.
Problem:
Conflicting metrics create a lack of trust in the data.
Solution:
- Use a standardized data model across all business units.
- Establish who is responsible for the data being captured, and how it is being used to report metrics.
- Integrate analytical reports into workflows.
Result:
- Decisions are made quickly and are:
- Aligned with the organization’s overall goal and objectives.
- Improve overall company results.
Alignment creates outcomes from data, not supporting the infrastructure.
Next Evolution in Data Infrastructure and Analytic Systems.
1. Decision Intelligence:
Systems will automatically recommend actions, as opposed to people recommending the best course of action based on data.
2. Artificial Intelligence:
Systems will automatically generate insights from data.
3. Unified Data Platforms:
Systems will become less fragmented thus allowing people access to a single platform instead of duplicating efforts across many platforms.
4. Data as a Product:
Teams will own their data products and service those data products, creating a common focus on the product.
The future is not defined by having access to more data; the future will be based on your ability to make better decisions.
Conclusion: Build Your Systems Around Making Decisions, Not Just Collecting Data.
The gap between Pipelines and Decisions is not a temporary issue; rather, it is a structural problem.
To fix this gap will require a change in how business leaders and technology implement their respective solutions and systems.
- Change how they think about the (infrastructure) systems they develop or use.
- Change their focus from building pipelines to supporting decision-making.
- Change their focus from data availability to data usability.
At Logiciel Solutions, we do our part to help organizations design artificial intelligence-first data infrastructure and analytic systems to connect data and decision support.
In the end, the value of data does not come from how much data you produce but from how well you utilize the data you produce.
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Frequently Asked Questions
What is Data Infrastructure and Analytics?
The processes and systems to develop, collect, store, process, and analyze one’s data used for making business decisions.
Why do Companies Struggle with Making Data-Driven Decisions?
Misalignment between the business and data systems. Trust Issues. Fragmented Analytical Tools.
What Are Your Key Components of Modern Data Architecture?
Data Ingestion, Data Store, Data Transformation, Data Analysis, and Data Governor.
How can Organizations Improve Their Ability to Make Data-Driven Decisions?
Standardising all their metrics, increasing the accessibility of their data, and embedding analytics into their workflows.
What is the biggest challenge in data infrastructure today?
Finding a way to close the gap between the availability of data and having access to actionable insights.
