With the development of current data storage and processing technology, it is relatively easy and inexpensive for businesses to start owning a data warehouse or building their own reports/dashboards. But to gain value from that data, one must create a close link between business strategy and technology. This requires an investment in the right tools for each stage of the data lifecycle.
So what are the right tools? And how does one implement them properly to achieve maximum impact?
To answer that question, each company needs to know where they are in the journey of digitization and data platform development. At the same time, this journey needs to follow a methodical approach because it will take time to convince the staff of the value of technology and to accept the transformation of habits toward digitization.
The following 4-stage data maturity model, introduced by Dell Technologies and Intel, will answer the question: Where to start to become a data-driven business? What stage of the data evolution roadmap is your business? How will the company face the challenges at each stage, and what should be prepared?
A data-driven organization is a business that makes decisions based on data rather than on instincts, hopes, general observations, or personal opinions. And this applies to every level of the business.
Becoming a data-driven organization is more than simply buying and installing the necessary applications and tools, hiring data experts, and investing in your data infrastructure. It's about making data and analytics an important part of your overall business strategy, culture, and all business processes.
A data-driven organization typically has the following characteristics:
Employees at all levels can easily access and make use of data that is germane to their jobs.
Business leaders see data as an asset and rely on it to make decisions.
Teams use data to find consensus in meetings and group projects.
Dell's data development model is probably the most popular today. This model consists of four levels of data development, as shown in the figure.
Skip stage 0 - when data aggregation efforts are almost non-existent. At the Data Awareness stage, businesses recognize the value of data and begin manually collecting data from various systems. The enterprise has many operational tools and software, but it lacks database planning. The data is fragmented, the data quality is inconsistent, and the reports are tailored to needs, but accuracy is low due to a lack of multidimensional data collation across multiple sources.
If this describes your organization, you'll need to prioritize the following tasks in order to advance to the next stage:
Create a data model that reflects the business's value chain.
Using tools/software, collect data at each link of the value chain.
Construct a data pipeline to consolidate data into a single database.
Create and standardize a management reporting system to extract valuable insights from data.
Data quality has improved, and businesses have standardized data at the production site. Departments in the enterprise have clear, strict rules about which data to access and under what quality requirements. At this stage, businesses also begin tracking key performance indicators (KPIs) and experimenting with data-driven decision-making. However, most of the data used are still structured data; companies still lack experience in controlling & using unstructured data (image data, voice, etc.)
At this stage, businesses should focus on continuing to improve data quality and increase integration between applications, deploying new technologies to handle unstructured data. Optimize the data warehouse and implement a Master Data Management strategy. The ultimate goal is to establish all standardized reports on a centralized platform. By then, the organization has already begun to use its resources more efficiently.
At this stage, businesses are using data to make important business decisions. The business and IT departments are closely linked with leadership support to eliminate data fragmentation across the organization.
When business departments need to use data as a competitive advantage, it requires the development of corresponding information technology systems. The IT system must now respond by implementing new technologies that can integrate all data sources and applications to: (1) efficiently provide and store data; and (2) provide analytical reports that meet the needs of relevant departments.
The final stage of this model is Data-driven - all decisions in the organization are based on the database. The goal of this phase is to expand the data strategy while continuing to cut costs. The IT department and the business function as a tight, cohesive unit. The IT system can integrate all data sources, both inside and outside the platform, and apply advanced analytical methods. The business has identified where and how to embed analytics in its processes.
The challenge at this stage is to seamlessly embed data analytics into operational workflows, expand the database, and move from descriptive and predictive analytics into prescriptive analytics.
Becoming a data-driven organization enables end users to perform their own data analytics without IT support, on a trusted, supported architecture.
There are many methods and models to transform your business into a data-driven organization. Dell Data Maturity Model is one of the most popular and methodical models. Hope this article can help you map out some great ideas for the path to becoming a data-driven business.
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More on the same problem:
Your Small Business Has Plenty of Data but You Still May Not Know What Is Happening (video)
Zoho CRM Custom Module vs Subform: How to Model Related Data (Without Repainting Later) (article)
Ask a few practical questions. Can a manager get last month's numbers without asking someone to build a spreadsheet? Do two departments report the same figure for the same thing? Is there a single place where customer, order and financial data meet? Are decisions in leadership meetings argued from data or from opinion? Honest answers place most mid-sized businesses at the data aware or data proficient stage, whatever their tooling suggests.
Each step usually takes a year or more, because the work is as much about habits as about systems. Moving from data aware to data proficient means consolidating sources, agreeing definitions and getting people to trust the reports, which takes repeated cycles of use and correction. Later stages depend on integrating applications and on leadership using the data in front of the company. Rushing a stage tends to produce dashboards nobody believes.
Early on, one person who owns the data model and the reporting, often an analyst or an operations lead with the authority to settle definitions. At the proficient stage, someone responsible for data quality and integration between applications, which may be an engineer or a systems partner. At the savvy and data driven stages, analysts embedded in departments and a leader who owns the data strategy. Titles matter less than clear ownership of definitions.
Disagreement about what the numbers mean. When sales, finance and operations each define a customer, an order or revenue differently, no amount of tooling produces a report everyone accepts, and people fall back on opinion. The second blocker is data trapped in disconnected systems, which forces manual collation and makes the numbers late and doubtful. Fixing definitions and connections comes before analytics at every stage of the model.
It needs the questions the model asks, even if it never uses the labels. A small business benefits from knowing where its data lives, which numbers it trusts and which it does not, and what the next sensible step is. The model's value is sequencing: it stops a company buying analytics tools before its data is consolidated, or hiring a data scientist before anyone can agree on last quarter's sales figure.
Pick measures that reflect behaviour rather than tooling. The share of regular reports produced automatically instead of by hand, the number of agreed definitions in a shared glossary, the time between month end and reliable numbers, the count of applications integrated into the central data store, and how often leadership decisions cite a report. If those move, maturity is moving, whether or not the stage label has changed.
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