Instead of utilizing built for purpose data management tools, businesses in the early stages of a data strategy often leverage pre existing, make-shift software.
However, the rate in which modern businesses create and store data, means these methods can be quickly outgrown.
In our last post, we looked at why any business with current, or future plans for a data-driven strategy need to ensure a strong data foundation is in place.
Without this, the insight provided by data can often be incomplete and misleading. This negates many of the benefits data strategies are typically implemented to find, and can cause problems down the line such as slowing down time to markets; increasing the potential for missteps and false starts; and above all else, adding to costs.
Leveraging a combination of data management tools, including data modeling, enterprise architecture and business processes can ensure the data foundations are strong, and analysis going forward is as accurate as possible.
For a breakdown of each discipline, how they fit together, and why they’re better together, read on below:
This post is part two of a two part series. For part 1, see here.
Data Modeling
Effective Data Modeling helps an organization catalogue, and standardize data, making the data more consistent and easier to digest and comprehend. It can provide direction for a systems strategy and aid in data analysis when developing new databases.
The value in the former is that it can indicate what kind of data should influence business processes, while the latter helps an organization find exactly what data they have available to them and categorize it.
In the modern world, data is a valuable resource, and so active data modeling in order to manage data, can reveal new threads of useful information. It gives businesses a way to query their databases for more refined and targeted analysis. Without an effective data model, insightful data can quite easily be overlooked.
Data modeling also helps organizations break down data silos. Typically, much of the data an organization possesses is kept on disparate systems and thus, making meaningful connections between them can be difficult. Data modeling serves to ease the integration of these systems, adding a new layer of depth to analysis.
Additionally, data modeling makes collaborating easier. As a rigorous and visual form of documentation, it can break down complexity and provide an organization with a defined framework, making communicating and sharing information about the business and its operations more straightforward.
Enterprise Architecture
Enterprise Architecture (EA) is a form of strategic planning used to map a businesses current capabilities, and determine the best course of action to achieve the ideal future state vision for the organization.
It typically straddles two key responsibilities. Those being ‘foundational’ enterprise architecture, and ‘vanguard’ enterprise architecture. Foundational EA tends to be more focused on the short term and is essentially implemented to govern ‘legacy IT’ tasks. The tasks we colloquially refer to as ‘keeping on the lights’.
It benefits a business by ensuring things like duplications in process, redundant processes, and unaccounted for systems and shelfware don’t cost the business time and money.
Vanguard enterprise architects tend to work with the long term vision in mind, and are expected to innovate to find the business new ways of reaching their future state objectives that could be more efficient than the current strategy.
It’s value to a business becomes more readily apparent when it enterprise architects operate in terms of business outcomes, and include better alignment of IT and the wider business; better strategic planning by adding transparency to the strategy, allowing the whole business to align behind, and work towards the future objective; and a healthier approach to risk, as the value (reward) in relation to the risk can be more accurately established.
Business Process
Business process solutions help leadership, operations and IT understand the complexities of their organizations in order to make better, more informed and intelligent opinions.
There are a number of factors that can influence an organization who had been making it by without a business process solution, to implement the initiative. Including strategic initiatives – like business transformation, mergers and acquisitions and business expansion; compliance & audits – such as new/changing industry regulations, government legislation and internal policies; and process improvement – enhancing financial performance, lowering operating costs and polishing the customer experience.
We can also look at the need for business process solutions from the perspective of challenges it can help overcome. Challenges including the complexities of a large organization and international workforces; confusion born of undefined and undocumented processes as well as outdated and redundant ones; competitor driven market disruption; and managing change.
Business process solutions aim to tackle these issues by allowing an organization to do the following:-
- Establish processes where they don’t exist
- Document processes that exist but aren’t consistently followed
- Examine/analyze/improve/eliminate processes that don’t work
- Optimize processes that take too long, cost too much or don’t make sense
- Harmonize redundant processes across the organization.
- Construct processes for new products, markets and organizations
- Disrupt processes with new technology and data assets.
The Complete, Agile Foundation for the Data-Driven Enterprise.
As with data, these three examples of data management tools also benefit from a more fluent relationship, and for a long time, industry professionals have hoped for a more comprehensive approach. With DM, EA and BP tools that work in tandem with, and complement one another inherently.
It’s a request that makes sense too, as although all three data management tools are essential in their own right, they all influence one another.
We can look at acquiring, storing and analyzing data, then creating a strategy from that analysis’ as separate acts, or chapters. And when we bring the whole process together, under one suite, we effectively have the whole ‘Data Story’ available to us in a format we can analyze and inspect as a whole.