We help you define, prototype, develop, deploy and manage advanced analytic models that can be trained to continually improve accuracy, relevance and quality. Here’s some of the typical customer challenges and opportunities we see.
DataScience - Element - KeyMetrics
We need to get more scientific about forecasting our key business metrics.
False
DataScience - Element - Customers
We want to understand our customer profiles better so we can have more targeted conversations.
False
DataScience - Element - Pricing
We need to optimise our pricing strategy to maximise revenue (and/ or other KPIs).
False
DataScience - Element - Manual
Our paperwork processing is too time-consuming, manual and repetitive. We need to automate.
False
DataScience - Element - Migration
We’re moving to open source (e.g. from SAS to R/Python) – but we lack in-house skill and experience.
False
DataScience - Element - Profile
We need to raise the profile of data science in our business with roadmaps, internal stakeholder engagement & technical guidance.
False
DataScience - Element - Resources
We need data science resources to help deliver projects due to lack of skills and/or capacity.
False
DataScience - Element - Production
We have previously developed models but they need to be productionised to improve and generate value over time.
False
0
Grid
Four Columns
White
Center
With Borders
Small Image
Default
Empty Space
Empty Space
False
Diagram_Our resources and capability - DataScience
Our resources and capability.
The scale and skills of our talented teams (and where to find them).
As a building block of AI and a core discipline of data science, we develop and deploy right-fit machine learning models that reflect your data maturity. To ensure these models stay relevant and deliver ongoing value, we apply MLOps best practices to optimise scale, supportability and stability.
False
Value Proposition Inverted
False
DataScience - Advanced Stats
DataScience - Advanced Stats
Advanced statistics.
In the commercial world, Data Science is all about applying relevant mathematical, statistical and computational techniques to create maximum business value and impact.
Our experienced data scientists apply frequentist and Bayesian statistical techniques to tackle business challenges, such as improving estimate accuracy, A/B testing frameworks and numerical optimisation.
False
Value Proposition
False
DataScience - Analytic Development
Empty
False
DataScience - Analytic Development
Analytic development.
We develop analytic software including packaged code in R and Python for data scientists, data processing backed web app based on Shiny, Dash and other similar frameworks, open source transformation and related code conversion.
False
Value Proposition Inverted
False
DataScience - Learning & Development
DataScience - Learning & Development
Learning & development.
According to a DataIQ survey in June 2021, maturing your data science function can deliver an uplift of ~14% of your revenues.
Our senior resources provide technical, strategic and operational guidance to your in-house teams, from classroom training to consulting/ mentoring and tactical/ longer term team augmentation.
Led by business objectives and motivated by delivering real business impact: value and outcome-driven.
Accelerate change and drive momentum in the re-investment cycle by delivering early value.
IDEaL Framework: Investigate, Develop, Evaluate and Launch are the steps in a full delivery cycle.
Investigate phase de-risks development process. Clearly articulated success criteria, business alignment and minimised risks of project failure.
MLOps: the application of CI/CD and other best practices (e.g. data drift monitoring) to automate ML tasks in experimentation and production.
Continuous improvement in model performance and longer term value delivery.
Hive knowledge: we promote a learning and development culture as a team of curious minds, sharing knowledge and latest developments to ensure high delivery quality.
Benefit from the latest practices and proven techniques we accumulate over time and across industries.
True
Base
False
IN ACTION
DATA - DataScience CaseStudy
Heathrow Airport
Forecasting airport baggage flows.
As Europe’s busiest airport, Heathrow safely carries hundreds of thousands of passengers through its terminals and onto their flights every day. Its busiest terminal, T5, carries over 30 million passengers each year, so even small advancements in planning and passenger efficiency can have enormous impact on total capacity for the airport as a whole.
We helped Heathrow to build a predictive modelling forecast for future baggage handling requirements based on historical patterns and future flight schedules, including the impact of unplanned events and variants such as weather and air traffic control issues system problems.
Logo - Data - Case Study - DataScience Heathrow
Logo
False
Career Progression Services
False
Empty Space
Empty Space
False
Our customers.
We love what we do and we get to work with some of the sharpest minds in the brightest businesses: from smart home devices, space exploration and beer to manufacturing, finance, ecology and logistics.
Creating compelling omnichannel experiences from bar to browser.
BREWDOG
Optimising performance & support with 360° insight into the elite Women’s game.
ENGLAND & WALES CRICKET BOARD
Democratising data to engage new communities & protect the UK seabed.
THE CROWN ESTATE
Delivering the horizontal scale to expand into new medical research fields.
HANSON WADE
Improving experience & making life simpler for home automation customers.
HIVE
Bringing on-demand to the UK’s favourite TV listing and review platform.
RADIO TIMES
Reducing cost, accelerating innovation and attracting new talent in healthcare.
Organisations on a data-driven journey want to achieve value from their data, but what they are attempting isn’t really about digital, first and foremost — It’s actually about the necessity of transforming business models.
Download this whitepaper to learn how to get started on your data-driven journey, and discover the pillars that define a successful transformation.
Do you know who owns the data in your organisation? Should you care?
Blog
Branka Subotic, Ascent’s Principal Data Consultant looks at the various data roles within an organisation and the business-wide responsibility to make data-led business decisions.
data ownership, data owner, data steward, technical strategy, data & analytics, data-driven, data custodian
True
True
False
2021-12-12T00:00:00Z
Do you know who owns the data in your organisation? Should you care?
Data is arguably the biggest asset an organisation owns - but who’s ultimately responsible for it? Ascent’s Principal Data Consultant Branka Subotic considers the roles and responsibilities of data ownership.
Have you ever sat in a meeting with your Board or executive team when a really obvious question was asked that nobody truly had an answer for? Do you recall an uncomfortable silence followed by a senior leader providing a half-baked response (whilst two other senior leaders frantically messaged their teams)?
If you do (and you are not in the minority!) - ask yourself this: who should have had the answer? Who owns data in your organisation?
Data should drive ALL business-critical decisions.
The Covid-19 pandemic has disrupted (and is still disrupting) most industries. For some, it has led to a complete standstill for few months, and an urgent need to re-finance and cut costs. For others, it has meant a boost in sales and unprecedented growth.
Regardless of where your business stands in between these two extremes, you can say that it has taught us all how important it is to have accurate, readily available data that informs critical business decisions. Often, this is data which describes productivity per location, per sector, per type of product, per team, per employee, or it simply indicates the actual number of products sold, customers engaged, or employees in the company.
Let’s run with the latter example. If you ask HR how many employees there are, you will get a figure including everyone who has a contract with the company, permanent staff as well as contractors, but also staff who are on unpaid leave, special leave, sabbatical, secondment, etc.
If you ask Finance, you will get a number that reflects staff on the payroll. Therefore, the answers to the same question from HR and Finance will be different – but both can be considered ‘correct’. But which answer should you use to drive your business?
Governance and trust: data roles.
Data and analytics assets exist everywhere across an enterprise and vary in nature – and not all data and information is equal. Gartner suggests establishing a trust-based governance model that:
supports a distributed ecosystem of data and analytics assets
acknowledges the different lineage and curation of these assets, and
assists business leaders in making contextually relevant decisions with greater confidence.
The last point above is key - it all comes down to context. If we consider our earlier example, the scenario might be that the CEO is asking how many employees the company has because they need to decide how many they will furlough. Providing this kind of answer is only possible if the ‘People’ data in this company has a single owner who has a framework in place to steward the relevant data sets and deliver context-specific, relevant answers to organisational questions.
Which brings us to data governance roles. There are various approaches to the delineation of responsibilities around data but one of the simplest (and therefore my favourite), is the distinction between Data Owner, Data Steward and Data Custodian. You can read vast amounts of material on each of these roles from either Gartner or DAMA, but, succinctly, this is what they mean to me:
A Data Owner is the person accountable for the specific and logical groups of data assets (in our example, all data sets that constitute ‘People’ data), whether generated by the company or 3rd party (e.g., postcode database). The Data Owner can be a member of the executive team or a senior manager with delegated authority and a vested interest in ensuring data is managed appropriately.
A Data Steward is responsible for maintaining specialist knowledge about their data area, putting into place acceptable use of this data, maintaining necessary records about the data (metadata) and is consulted for operational advice regarding any changes about the acquisition, transformation, storage and consumption of this data (where consumption includes both human and system usage). They implement data strategy enterprise-wide for their data area and are also responsible for performing any transformations required for their data assets.
A Data Custodian is responsible for a set of data. Data Custodians are essentially data administrators who focus on the ‘how’, rather than the ‘why’ of data management. Data Custodians must communicate and collaborate with the Data Steward regarding any technical activities that impact the data within the Data Steward’s scope.
Here’s how that looks in practice:
Data governance ensures that the right people are assigned the right data responsibilities. It is mostly about strategy, roles, organisation and policies, whilst data stewardship is all about the execution and operationalisation of said policies for the benefit of the whole business, making sure that the data is accurate, in control, and easy to discover and process by the relevant parties.
NB: It is very important we do not mix Data Stewardship in any way with the business function within which the Data Steward happens to sit. The role they perform is company-wide.
In our previous example, the Data Steward for the ‘People’ data may well sit in the HR department, but they are responsible for the single source of truth for a total number of employees, staff demographics, contact details, licences/ qualifications and their validity, etc. Similarly, the Data Steward for the ‘Customer’ data could easily sit in the Commercial department, but their remit is to manage a complete and accurate set of customer data for the whole of the business.
“So what?”, you say. Why should you care about all of this?
It all comes down to a single source of truth. When your Executive asks a question, you want to make sure there is a single party responsible for getting to the answer, using a managed, quality-checked data source or sources. You want to prevent different parties going off on a tangent trying to answer the same question in silos, using locally produced data sets that are not quality checked, resulting in different answers, delivered in different formats with a range of differing assumptions.
What is good is to start asking this question today (not next week, or the week after). The longer you let the business evolve without a clear answer to who the data owners are, the longer you will lack clarity about your business, its performance, and clear lines of accountability.
So see your data for the asset that it is: go ahead, be brave, ask the question. And if you need a hand, the Ascent team is here to help you every step of the way!
Branka Subotic
Principal Data Consultant
Ascent
A strategic thinker, Branka is passionate about data, specialising in strategy and transformation. Branka’s primary role at Ascent is to help customers turn data into insight to support operational decision-making, having established her credentials in a mission-critical context: leading key alliances and advanced analytic teams in European air traffic management for over 15 years.
Branka is also a Chartered Engineer with a PhD in air traffic management, an MSc in aeronautical science and an MEng in air transport engineering.