Many companies struggle to utilise their data effectively resulting in duplicate sources of information, a lack of understanding of how data can be used to enhance your customer experience, inefficient internal operations and missed commercial opportunities. The practice of big data architecture has been refined over the last decade and the principles driving this architecture can and should be utilised by all organisations looking to collect, store, process and use data they have captured. Peru Consulting big data architects can help you navigate the buzz words and terminology that plague the big data industry and provide you the right capabilities to help your data programmes.
Modern Data Architecture
Modern data architecture consists of understanding how an organisation wants to use their data strategically and how access to better data can enable a business to grow and achieve its ambitions. The ubiquitous use of public/hybrid cloud services, the multitude of data warehouses and legacy of batch processing, and the huge ecosystem of machine learning and AI tools make it difficult (and expensive) to derive any meaningful value from your data. Siloed enterprise applications housing their own data stores of structured and unstructured data complicate things further and the ability to manage data in these environments is compounded by a lack of ownership and cultural understanding of data assets.
Data architecture practices should be about providing decision-makers ready access to business insights, transforming information to knowledge through analytics, providing customer insights through trend analysis and supporting the increased focus on automation. Ultimately if you are embarking on a digital transformation journey you need data to accelerate that journey and data architecture will help you navigate a path that supports your business.
Data Storage Architecture
The onboarding and collection of data is a critical element of data architecture and one that often gets left in the hands of traditional Infrastructure and Operations teams that have no experience in the running and scaling of enterprise-wide data services. In order to establish best-fit storage architecture, you need to get close to the owners of the workloads and applications that will be leveraging the environments. Also, it’s not just about storage – processing, protection, integration, and life cycle management of data are equally important. Establishing a company-wide data catalog should also be a key part of data storage architecture aims: the catalog should specify SLAs, delivery models, and resilience requirements needed to be used to generate KPIs and foster transparency with consumers of the data services.
Why Does your Company need Data Architecture Consulting?
There are many benefits of data architecture consulting that your company can realise. At a high level, it’s about gaining the data skills internally that allows you to respond quickly to your business data needs, support intelligent automation initiatives to drive internal efficiencies, provide data insights not previously visible, and drive a competitive edge. We are seeing broad trends which are motivating organisations to focus more on data and bring in necessary skills:
Regulatory Compliance: How can we automate data transparency and auditability while responding to an ever-changing regulatory landscape?
Data Utilisation & Automation: Where does our data reside and how do users access it to drive efficiency & increase accuracy in your organisation?
Enhanced Analytics: How can we derive improved insight from our data instead of wasting effort on costly data wrangling?
Data Monetisation: How can we monetise our data to gain a real competitive advantage and where do the opportunities lie?
Data Architecture Consulting can help you respond to these trends delivering:
- Improved Decision Making through available and relevant data
- Delighted Customers through products and services that meet their need, when they need it at a cost they can afford
- New marketing opportunities enabled through business insights derived from your data
- Enhanced MI to drive effective and efficient internal operations
- Monetisation of data to generate new revenue streams
Data Analytics and Machine Learning
The main focus for a lot of organisations is to build a data platform of structured and unstructured data to drive analytics capabilities. Better analytics is, for many organisations, the main outcome from most data initiatives. It’s understandable why that would be the case with analytics removing the need for costly manual data manipulation. Analytics though has been on the agenda for over 20 years but it’s only now that it’s become possible – with the advent of machine learning – to derive real valuable insight identifying current and future trends. Legacy investment in data warehouses and data lakes can now bear fruit by applying modern data processing, enrichment and meta data. Technology that allows for the linking of seemingly disparate data sets have unlocked potential that really contribute to the benefit of the organisation. Analytics have had a new lease of life and we can help you uplift your legacy investments in data using new technology and approaches to data management.
In most organisations the management of data is treated as secondary to the core of business operations. Organisations have not created a focused area for the management of data or content, and many have no executive responsible for using data in an organisation effectively and for identifying competitive advantage. Most enterprise data or knowledge initiatives are focused on projects rather than trying to develop enterprise-wide capabilities such as fostering communities or collaborating to create and share value from knowledge.
An effective knowledge capability can only be supported by a robust data management programme. The practices developed through this programme must be sustaining and form a core part of the standard operations. IN order to enact a robust data management approach organisations must focus on developing an appropriate data operating model, building out meta data management capabilities, and focusing on data quality.
Data Operating Model
The operating model covers all competencies needed to execute a successful data management programme: this covers principles, the scope of data sources, a portfolio of use cases to achieve stakeholder goals, and the governance framework supporting that. Additionally, this covers the roles, processes, structures, and technology needed to support the delivery of data management.
Metadata Continuum (Management)
Metadata provides data about data – it describes its location, its use, and how it is related to other pieces of data. The management of metadata is a key enabler for modern data-led enterprises. Metadata management can help support regulatory compliance (GDPR), support data reuse by identifying the most valuable data, improved business productivity by identifying where data resides and how to use it and can help in impact and dependency analysis when introducing new programmes of work or new applications. Key elements could be a data catalogue, business glossary, and data lineage repository.
Data Quality Management
Most organisations lack essential data quality roles or don’t know where to place them for optimal impact. Additionally, there is a lack of measurement in the annual financial cost of poor data leading to difficulty in dedicating resources to this area – crucial to effective data management. The assignment of data stewards and owners are a key part of successful data quality initiatives and sustaining quality management practices. Data quality has many dimensions, and it is important to assess which dimensions are appropriate for each data entity – not all will apply. Responsible data owners/stewards and data management teams will assess against these dimensions and will use meta data capabilities to support quality efforts.
Data and Digital Transformation
Data is a key component of digital transformation efforts; without effective data, it will be impossible to improve customer experience and engagement. Without data, it would be impossible to share, link, and deliver data in support of cross-channel collaboration processes. Data is the key fuel that drives digitisation. This is particularly true when building enhanced operational capabilities by augmenting your processes with RPA and Intelligent automation technology; these technologies are only as good as the data available to support machines, people and systems needed to integrate across data flows. Acting upon data events as they occur to generate new opportunities, mitigate risk, or improve digital channel interactions is also a key enabler to this transformation.
Why Peru for data architecture consulting?
Peru has a proven data architecture framework toolkit based on leading industry standards that focuses on being led by the business and not being driven by technology choices. We are focused on accelerating value early through our flexible, agile and iterative approach to data architecture and data engineering delivery. We are technology vendor agnostic, choosing solutions based on cost & fit. We have an industry recognised approach to maturity roadmaps guiding you to prioritise use cases and focus on quick wins and early POCs to prove strategic direction & deliver data maturity.
Key activities often include:
- Data Discovery: to understand the data you have, and where it resides and how it is accessed
- Data Health Check: to determine the accuracy of your data and increase your confidence in utilising it
- Data Maturity Assessment: to understand how you compare to your competitors and industry best practice
- Architecture Function review: to understand your teams’ culture, skills and processes