We already know data is powerful.
It helps businesses understand their customers, track their performance, and make more informed decisions.
To be truly useful, however, this data must be accurate, timely, accessible, relevant, and secure at all times - a challenge that enterprises have solved with modern data architectures.
But, as the infamous business adage goes, the solution to a problem is often another problem, and so it is with data architectures.
The exponential growth in the number of data assets, channels, and environments has increased the complexity of enterprise data architectures so much that key data stakeholders, instead of optimising the system, spend most of their time and energy firefighting in order to ensure it keeps functioning.
Often, just ensuring that the relevant data can be used by business users to complement decision-making is considered a win, while fingers are crossed in the hope that ungoverned data processes don't lead to restricted data being accidentally viewed, shared, or leaked by unauthorised users, both within and outside the organisation.
As a result, few enterprise stakeholders are able to focus on the real question: is their current data architecture even working properly?
Defining your data architecture: Why you need to build data flows around specific business needs
As a Chief Data Officer (CDO) or Chief Information Officer, it is important to understand that there is no one-size-fits-all answer when it comes to data architectures.
The data architecture of a small business with a few hundred employees will be very different from that of a large multinational corporation with thousands of employees in multiple countries.
Similarly, the data architecture of a manufacturing company will be different from that of a retail company. This is because the data needs of each business are different. Even two businesses of comparable sizes operating in the same sector will need different data architectures; what worked for another business will not necessarily work for you.
It is also critical for key stakeholders - such as CDOs, data scientists, and data architects - to remember that data architecture is the blueprint that governs the flow of data within the organisation to support and achieve business goals, not the platform or solutions deployed to achieve this result.
For instance, if the goal is to improve customer experience, then the data architecture should be designed to help the organisation collect, process, and store customer data so that it can be used to improve customer experience.
Similarly, if the goal is to improve operational efficiency, then the data architecture should be designed to help the organisation collect, process, and store data that can be used to improve and optimise processes and workflows.
Clearly defining these objectives will allow you to create a clearer roadmap of how enterprise data will be accessed, utilised, stored, and shared - which, in turn, allows you to identify the platform and solutions that can help you achieve those goals.
Defining the goals of the data architecture and selecting the data platform to deploy it, however, is just half the battle.
To work as designed, any data architecture needs a strong buy-in from stakeholders across the organisation. This is easier said than achieved.
Larger organisations often have multiple teams, both internal and external, which collaborate on time-bound projects. In most such instances, functionality takes precedence over efficiency, with getting there more important than getting there efficiently.
There is also a mental block in play.
People don't like changes to patterns and behaviours that they are comfortable with, which makes switching over to newer data tools, workflows, integrations, and solutions a difficult task.
When users don't trust the data as they don't understand how the data was generated or why it is relevant to them, they default to old data practices that they are familiar with - defeating the purpose of such data deployments.
Trusting the data: How to ensure stakeholder buy-ins for successful data deployments
This is why it is important to involve all stakeholders in the data architecture deployment process, including CXOs, business users, IT teams, data scientists, and data analysts.
Each stakeholder group has its own specific needs, and these need to be considered when designing the data architecture. Doing so will not only ensure that the data architecture is designed to meet the needs of each group but also handhold them through the process of its adoption.
Seeing where their data comes from and what kind of value is added each step of the way, fosters trust in the data and enables faster adoption of new architectures.
A good example of the issues faced with trust in data architecture deployments is the long-standing challenge of combining SAP and non-SAP data.
Most organisations use SAP data in some shape or form, and teams working directly with SAP systems are notorious for not wanting to 'contaminate' their internal data with data from other systems and environments.
This is understandable; SAP data is extremely reliable and secure, with a proven history of delivering consistent results. Any attempts at such data integrations have also been, up to now, hindered by the business and technical challenges of migrating proprietary SAP data into other environments.
New-age data integration platforms such as Qlik Data Integration address these issues. They swiftly map SAP business processes across the organisation and combine it with data from other sources to generate more in-depth schemas and calculations.
As a result, data that business users trust is augmented by complementary, real-time data that not only reduces the time taken to generate actionable insights but also provides a richer context for more accurate, agile, and flexible decision-making that unlocks hidden value across data warehouses and big data systems.
Recently, Qlik also became a foundational partner for Google Cloud Cortex Framework which helps support SAP users in delivering innovation and business outcomes faster with a rich set of data foundation building blocks including predefined operational data marts, change data processing, and machine learning templates for common business scenarios.
With this partnership, the powerful combination of Qlik and Google Cloud solutions help take the guesswork out of delivering analytics and insights at scale and accelerates time to value.
Evaluating the impact of data deployments for better business outcomes
Organisations need data like a body needs oxygen, and a successful data architecture ensures that every business function has access to all the relevant information needed to make accurate and timely decisions.
This is only possible if the deployed data architecture strikes the right balance between technical specifications and more intangible aspects such as business need fulfilment and stakeholder trust. So, if you're a data stakeholder looking to optimise the value of your data initiatives, ask yourself: is your data architecture doing okay?