Our data servicesIncorvus’ data services can help you realise the value of your data Cinderella!
Data is now your most valuable & critical asset – you can’t ‘do digital’ without it!
See & understand what your data is telling you; spot risks & rewards: find out where you are leaking value or laying up hidden costs.
PREPARATION
From audit to validation.
Data audit
Data audit: an agile risk / reward, gap analysis of your data domain.
Data discovery
Data discovery: locate, source, extract, catalogue & assess the data the business needs.
Discovering data sounds routine but organisations may be unaware of or do not ‘understand’ approximately 80% of their data .
How much of your data swamp do you actually use, need or understand? Is it time to ‘discover’ your data?
Discovering data sounds routine but organisations may be unaware of or do not ‘understand’ approximately 80% of their data .
How much of your data swamp do you actually use, need or understand? Is it time to ‘discover’ your data?
The risks of undiscovered data
Data management
With data as key organisational asset in the digital age, data management is key to the pursuit of information delivery.
Overseeing data from raw source to value-add output must echo corporate strategy whilst fulfilling its traditional remit.
Overseeing data from raw source to value-add output must echo corporate strategy whilst fulfilling its traditional remit.
don’t fail to prepare!
Data quality
Data quality: profile, clean, describe and remediate quality data to meet business needs.
Duplicate or incomplete entries; poor or missing metadata; and parsing inconsistencies are common symptoms of data quality issues.
What is the opportunity cost of their adverse impact on the business and its systems?
Duplicate or incomplete entries; poor or missing metadata; and parsing inconsistencies are common symptoms of data quality issues.
What is the opportunity cost of their adverse impact on the business and its systems?
why data quality matters to the business
why data quality matters to the business
Data transformation
Data transformation: convert, harmonise, aggregate, integrate or synchronise data for optimal processing.
Transformative processes such as converting, harmonising, aggregating, integrating or synchronising data; or dataset harmonisation should precede data processing for optimal performance.
Is your data processing optimised?
Transformative processes such as converting, harmonising, aggregating, integrating or synchronising data; or dataset harmonisation should precede data processing for optimal performance.
Is your data processing optimised?
TRANSFORM & OPTIMISE BEFORE YOU MIGRATE!
Data validation
Data validation: ensure the integrity, consistency & accuracy of data you intend to use as the basis for decision-making.
Is your decision-making supported by reliable, valid, trusted and available data within the required timeframe. If your data, its availability and timeframe are not validated, how confident is your decision-making?
Is your decision-making supported by reliable, valid, trusted and available data within the required timeframe. If your data, its availability and timeframe are not validated, how confident is your decision-making?
VALIDATING DATA & ITS DIMENSIONS
PRODUCTION
From data to information.
Information architecture
Information, like buildings, needs purpose-built design for optimal results.
Many institutions remain bonded to application-centric structures, strangled by overlapping integration, which do not meet this test of intent.
Many institutions remain bonded to application-centric structures, strangled by overlapping integration, which do not meet this test of intent.
DOES YOUR INFORMATION ARCHITECTURE REALLY MERIT THAT TITLE?
Data engineering
Data engineering: the optimal provisioning of data to information systems.
Data engineering is the application of engineering principles to ensure optimal flow, collection, availability, processing & protection of data as a business-critical resource.
The smooth, consistent, routing & flow of data throughout its lifecycle is an essential aspect of a healthy, data-centric ecosystem.
Data engineering is the application of engineering principles to ensure optimal flow, collection, availability, processing & protection of data as a business-critical resource.
The smooth, consistent, routing & flow of data throughout its lifecycle is an essential aspect of a healthy, data-centric ecosystem.
OPTIMISING DATA CIRCULATION
Data workflow
Data workflow: the operational flow & availability of domain data in a (quasi) real-time world.
Data migration
Data migration: wrangling data for smooth ETL when migrating to cloud or other systems.
The performance of Cloud is adversely impacted by unusable or unnecessary data which attracts equally unnecessary costs, particularly upon exit.
Clogging Cloud with poor or unnecessary data will also exacerbate any bandwidth choke points on your network. Is your shiny new Cloud choking?
The performance of Cloud is adversely impacted by unusable or unnecessary data which attracts equally unnecessary costs, particularly upon exit.
Clogging Cloud with poor or unnecessary data will also exacerbate any bandwidth choke points on your network. Is your shiny new Cloud choking?
NO DATA MIGRATION SURVIVES FIRST CONTACT WITH THE ENEMY
Data modelling
Data modelling: In the connected digital world, information architecture scenario planning.
As data volumes grow exponentially, modelling for conceptual & semantic integrity is a vital if you want to turn data into information, into knowledge & subsequently value for the business.
Is your metadata equal to this task? Before you model, does the business have shared understanding?
As data volumes grow exponentially, modelling for conceptual & semantic integrity is a vital if you want to turn data into information, into knowledge & subsequently value for the business.
Is your metadata equal to this task? Before you model, does the business have shared understanding?
THE MEANINGFUL MODEL
Master data (MDM)
Master data (MDM): not so much a ‘golden record’ but rather vital organisational control data should systems fail.
Master Data Management focused on the creation of a ‘golden record’ within a data domain; a ‘single version of the truth’.
But does your ‘truth’ must have consistent meaning across applications, across the business & its entire ecosystem, virtually in real-time, with meaning and identity common to all stakeholders?
Master Data Management focused on the creation of a ‘golden record’ within a data domain; a ‘single version of the truth’.
But does your ‘truth’ must have consistent meaning across applications, across the business & its entire ecosystem, virtually in real-time, with meaning and identity common to all stakeholders?
MASTER IDENTITY, NOT DATA!
REALISATION
From information to value.
Data analytics
Data analytics: interrogate, visualise & report conclusions and insights.
Experience of top level information systems & BI delivery means we rapidly identify trends, exceptions or areas of insight into business issues.
The interrogation of large datasets benefits from a mix of technological & professional disciplines, data science & data analytics, which speeds the distillation of organisational value from raw data.
Experience of top level information systems & BI delivery means we rapidly identify trends, exceptions or areas of insight into business issues.
The interrogation of large datasets benefits from a mix of technological & professional disciplines, data science & data analytics, which speeds the distillation of organisational value from raw data.
ANALYTICS IS ABOUT TIME TO VALUE
Data curation
Data curation: the iterative process of maintaining a quality dataverse that continues to yield value.
Organisations routinely service fleet and machinery – why not data & its lifecycle?
Consider the counterfactual argument. What is the cost of not looking after your data – a corporate coronary as systems clog and AI flatters to deceive? Or worse?
Organisations routinely service fleet and machinery – why not data & its lifecycle?
Consider the counterfactual argument. What is the cost of not looking after your data – a corporate coronary as systems clog and AI flatters to deceive? Or worse?