Democratize data with digital invention
Coal, oil, and human potential were the three most consequential assets during the industrial revolution. These assets built companies, shifted markets, and ultimately changed nations. In the digital economy, there are three equally important assets for innovation: data, devices, and human potential. These assets holds great innovation potential. For any innovation effort in the modern era, data is the new oil.
In every company, there is data that can be used to find and meet customer needs. Unfortunately, the process of mining that data to drive innovation can be costly and time-consuming, so needs aren't discovered and solutions aren't created. Data democratization can solve this problem.
What is data democratization? It's the process of getting data into the right hands to drive innovation. This democratization process can take several forms, but they generally include solutions for ingested or integrated raw data, centralization of data, sharing data, and securing data. When data is democratized, experts around the company can use it to form and test hypotheses. In many cases, cloud adoption teams can build with customer empathy using only data, to rapidly meet customer needs.
Ways to democratize data
There are various ways to democratize data, but most include methods of collecting, centralizing, governing, and sharing the data. The following sections describe some of these methods. When you build a solution to a customer hypothesis, you should assess whether to democratize data, to what extent, and how to do it.
Share data
When you build with customer empathy, customer needs guide the solution. If the need is data, the solution enables the customer to interrogate, analyze, and report on the data directly, with no support from IT staff.
Many successful innovations start as a minimum viable product (MVP) that delivers data to the customer. An MVP is a version of the product that has just enough features to be usable by the customer. It shows the possible potential of the product in order to gather feedback from the customer. In this concierge model, an employee is the data consumer. That employee uses data to aid the customer. Each time the customer engages manual support, a hypothesis can be tested and validated. This approach is often a cost effective means of testing a customer-focused hypothesis before you invest heavily in integrated solutions.
The primary tools for sharing data directly with data consumers include self-service reporting or data embedded within other experiences, using tools like Power BI.
Note
Before you share data, make sure you've read the following sections. Sharing data might require governance to provide protection for the data. Also, if the data spans multiple clouds it might require centralization. If data resides within applications, you must collect it in order to share it.
Govern data
Sharing data can quickly produce a minimum viable product to use in customer conversations. However, to turn that shared data into useful and actionable knowledge, more is generally required.
After a hypothesis has been validated through data sharing, the next phase of development is typically data governance.
Data governance is a broad topic that can require its own dedicated framework, a matter that's outside the scope of the Cloud Adoption Framework.
There are several aspects of data governance to consider as soon as you validate the customer hypothesis. For example:
- Is the shared data sensitive? Data should be classified before being shared publicly to protect the interests of customers and the company.
- If the data is sensitive, has it been secured? Protection of sensitive data is a must for democratized data. The example workload discussed in Securing data solutions provides some references for securing data.
- Is the data cataloged? Identifying the nature of the shared data aids in long-term data management. Tools for documenting data, like Azure Data Catalog, make this process much easier in the cloud. Guidance regarding the annotation of data and the documentation of data sources can accelerate the process.
When democratization of data is important to a customer-focused hypothesis, make sure the governance of shared data is in the release plan. This protects customers, data consumers, and the company.
Centralize data
Data centralization leads to more meaningful reporting, ensures that the same data is available across the organization, and increases your ROI. When data is dispersed across an IT environment, opportunities to innovate can be extremely constrained, expensive, and time-consuming. The cloud provides new opportunities to centralize data. When centralization of multiple data sources is required to build with customer empathy, the cloud can accelerate the testing of hypotheses.
Caution
Centralization of data represents a risk point in any innovation process. When data centralization is a technical spike, and not a source of customer value, we suggest that you delay centralization until the customer hypotheses have been validated.
When you centralize, you need an appropriate data store for the centralized data. It's a good practice to establish a data warehouse in the cloud. This scalable option provides a central location for all your data. This type of solution is available in online analytical processing (OLAP) or big data options.
The reference architectures for OLAP and big data solutions can help you choose the most appropriate centralization solution in Azure. If a hybrid solution is required, the reference architecture for extending on-premises data can also help accelerate solution development.
Important
For some customer needs and solutions, a simple approach might be enough. The cloud architect should challenge the team to consider low-cost solutions to validate the customer hypothesis, especially during early development. This section on collecting data discusses scenarios that might suggest a different solution for your situation.
Collect data
The two primary forms of data collection are integration and ingestion.
Integration: Data that resides in an existing data store can be integrated into the centralized data store by using traditional data movement techniques. This is especially common for scenarios that involve multicloud data storage. These techniques involve extracting the data from the existing data store and then loading it into the central data store. At some point in this process, the data is typically transformed to be more usable and relevant in the central store.
Cloud-based tools have turned these techniques into pay-per-use tools, reducing the barrier to entry for data collection and centralization. Tools like Azure Database Migration Service and Azure Data Factory are two examples. The reference architecture for Data Factory with an OLAP data store is an example of one such solution.
Ingestion: Some data doesn't reside in an existing data store. When this transient data is a primary source of innovation, you'll want to consider alternative approaches. Transient data can be found in a variety of existing sources like applications, APIs, data streams, IoT devices, a blockchain, an application cache, in media content, or even in flat files.
You can integrate these various forms of data into a central data store on an OLAP or big data solution. However, for early iterations of the build-measure-learn cycle, an online transactional processing (OLTP) solution might be sufficient to validate a customer hypothesis. OLTP solutions aren't the best option for any reporting scenario. However, when you're building with customer empathy, it's more important to focus on customer needs than on technical tooling decisions. After the customer hypothesis is validated at scale, a more suitable platform might be required. The reference architecture on OLTP data stores can help you determine which data store is most appropriate for your solution.
Virtualize: Integration and ingestion of data can sometimes slow innovation. When a solution for data virtualization is already available, it might represent a more reasonable approach. Ingestion and integration can both duplicate storage and development requirements, add data latency, increase attack surface area, trigger quality issues, and increase governance efforts. Data virtualization is a more contemporary alternative that leaves the original data in a single location and creates pass-through or cached queries of the source data.
SQL Server 2017 and Azure SQL Data Warehouse both support PolyBase, which is the approach to data virtualization most commonly used in Azure.
Next steps
With a strategy for democratizing data in place, you'll next want to evaluate approaches to application development.