Digital Transformation is a common ‘buzzword’ with many different interpretations.
Essentially Digital Transformation (DT) has the following attributes and is heavily based on ‘Data’:
- Data driven process in which the Data becomes the primary asset
- The Data driven process will force a business process change or ‘transformation’
- The applications being built will use ‘newer’ technologies (cloud based) and are client-facing
- The Digital application is monetised and is usually subject to pressures of scalability, version control and frequent functional updates
- DT will include both business and IT system ‘transformations’ (procedural, organisational, cultural)
- Usually includes Machine Learning and Artifical Intelligence (analysis, operational automation)
Not all systems need ‘Digital Transformation’. Client facing applications which are under competitive pressure to offer improved end user experiences, new functionality, or methods of accessing and using data are prime candidates for DT and to be built (or re-built) in the Cloud.
New Data Architectures, eg. Data Mesh
A Data Mesh is a Data Model built around a Business Domain regardless of existing silos or most-often, processes. It looks at the type, volume, velocity and purpose of Data to generate new ideas, new technological processes and products. It will impact the organisation and demand Organisational Change Management (OCM) to take advantage of new processes and data driven insights. A Common Data Model or CDM is usually created to standardised the domain language used by the firm, in order to understand data flows, schemas, storage and usage.
Many firms are looking to implement a ‘Data Mesh’ within a transformed Data Archiecture which is usually built on an AWS Data Lake concept and may use EMR or Hadoop as part of the ingestion-storage-analysis process. A Data Mesh is a business-domain view of data and focuses on using data to create products.
Firms need to keep in mind that about 65% of ‘Big Data’ Projects fail in some way to achieve Business or IT objectives.
We can help firms with:
- Identifying which applications lend themselves to a DT program
- Assess, analyse, filter, and rank those applications
- Develop coherent, realistic plans to enable the DT
- Help engineer and build the underlying infrastructure or application as needed
- Deploy security and network configuration templates as needed
- Build a Machine Learning or Artifical Intelligence process mapped to clean data (ETL) and concrete business requirements
Common uses we have been involved with include retail systems, mobile applications, bank applications and distribution applications.
DT can use more advanced technologies such as NoSQL, Serverless, API platforms, Data streaming.
- Confirm, amend or create: Data-Logical-Conceptual models, architectures
- Where is the data, how good is it, how is it used?
- Data Warehouse aggregation in the Cloud [could be private or public deployment models]
- Use BI [Business Intelligence] and then PI [Predictive Intelligence to report and analyze the Cloud hosted data
- Understand the use of data and how it can be used to generate revenues (new models based on BI or ‘Big Data’ analysis) or reduce costs (logging for e.g.)
- Identify assets, systems and data models to move over to the Cloud
- Create an ROI and justification
- This includes identifying the conversion of legacy applications including code bases such as: Visual Basic, SQL, old Java versions, Cobol, and early versions of .NET and ‘cloudify’ these apps and code
- Consolidation of multiple applications into one powerful application
- Addition and development of new features and specifications
- Addition of mobile capabilities using native or non-native HTML5
- Deployment of cloud in various high-availability configurations
- Management and maintenance of the applications
- Based on Data and IS Transformation
- Move business processes into the Cloud, to transform client experiences, internal usage
- Client centric Data and systems organization in the Cloud, will allow the client to create new ideas, new products and services and competitive advantages, along with new revenue streams.
Data variety and velocity:
AWS offers a wide array of ‘Big Data’ services which can be leveraged to help ‘transform’ data usage and understanding. Data types, data varieties, data velocities are important to understand when ‘transforming’ systems and looking to create new applications.