We prime your data to deliver intelligence and insight, helping you answer critical questions and make better business decisions. Here’s some of the typical customer challenges we see.
Data Engineering - Element - Data Value
Data value is locked into structured formats & can’t meet our evolving business requirements.
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Data Engineering - Element - Disparate Sources
Our data originates from disparate, unmanaged sources & represents a security risk.
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Data Engineering - Element - Manual Rep work
We have to do manual, repetitive processing to prepare our data for consumption.
False
Data Engineering - Element - Timescales
We can’t make data available in the timescales required.
False
Data Engineering - Element - Environment Limitations
Environment limitations result in non-optimal workflows & poor experiences for our users.
False
Data Engineering - Element - Data Quality
Increasing concerns relating to data quality are eroding business confidence.
False
Data Engineering - Element - Storage
Our storage/maintenance costs are increasing and are not aligned with value delivery.
False
Data Engineering - Element - People Turnover
Data is becoming an increasing challenge to maintain due to staff turnover/skills gap.
False
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Diagram_Our resources and capability - Data Engineering
Our resources and capability.
The scale and skills of our talented teams (and where to find them).
Data preparation is all about discovery and implementation planning for data transformation projects - followed by data cleaning, anomaly detection, quality optimsation, integration and migration activities. Our teams of experienced data engineers have worked in every scenario from start up to enterprise and know exactly how to optimise the accessibility and quality of your data, so it can reach its commercial potential more quickly.
A standardised approach to frameworks and reference architectures for migration and integration keeps things simple and efficient, so we can apply our expertise to the custom elements of your project.
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Data Platform - Data Engineering
Data Engineering - Data Platform
Data platform.
We help businesses structure and implement data transformation initiatives that are driven by their vision and objectives and aligned with their maturity.
Working closely with you, we design and build right-sized, cost-effective cloud data platforms on Azure that harness native services to make your data more standard, more secure and easier to govern.
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Value Proposition
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Data Pipeline - Data Engineering
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Data Engineering - Data Pipeline
Data pipeline.
Taking you from proof of concept to production, we design efficient, reliable pipelines that collect, move, transform, store and present data into analysis-ready structures.
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What it means to you table - Data Engineering
How we work and what it means to you.
How it work & what it means to you.
How we work.
What it means to you.
Azure native, automation-first data engineering approach using serverless technologies.
Optimised, lean development and operational costs.
Rapid mobilisation and deployment. Reusable functions and data processing pipelines enable fast ingestion of data.
Part of a broad, mature data offering that combines data architecture, engineering, consulting and data science with design thinking.
Direct route to building compelling business services on top of your modernisation investment.
End-user focussed: prioritisation of accessibility and usability through strong metadata-focussed approach.
Get to the answers, results and decision points that you need more quickly and effectively.
Engagement options: Projects and DataOps.
Onward service path out of transformative/ modernisation projects geared to deliver high volumes of smaller scale changes in BAU.
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IN ACTION
DATA - Data Engineering CaseStudy
Carlsberg Group
Moving intelligence to the cloud.
Heritage beer brand Carlsberg’s progressive approach to business and brewing has made them a world leader in the sector. Founded in 1874, Carlsberg now has more than 140 brands in its portfolio, which spans core, craft & speciality and alcohol-free brews.
Ascent’s Lisbon-based data engineering team were engaged to help Carlsberg undertake a critical migration from on-premise BI systems to the Azure Cloud, implementing batch and streaming data processes and replacing old SSIS packages with PySpark notebooks. The team also used Databricks to build machine learning models to predict churn and profitability.
Logo - Data Engineering Case Study
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Career Progression Services
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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.
Our Chief Data Scientist, Rich Pugh, on data within successful digital transformation.
Watch & listen
With over 80% of organisations now engaged in a digital transformation with data at its core, there is clear appetite amongst leaders to evolve and create more data-enabled businesses. Ascent’s Chief Data Scientist, Rich Pugh and his team discuss what an organisation needs in place to ensure a transformation doesn’t grind to a halt before it starts delivering value.
Our Chief Data Scientist, Rich Pugh, on data within successful digital transformation. by Ascent
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2022-11-04T22:00:00Z
Our Chief Data Scientist, Rich Pugh, on data within successful digital transformation.
Organisations increasingly understand that data is critical to their future success.
With over 80% of organisations now engaged in a digital transformation with data at its core, there is clear appetite amongst leaders to evolve and create more data-enabled businesses.
However, many transformations struggle to get off the ground, or stutter at the early stages. In the first episode of The Data Conversation we will discuss the things an organisation needs in place to ensure a transformation doesn’t grind to a halt before it starts delivering value. For this discussion I will be joined by Branka Subotic & Jon Stafford, both Principals in the Data Consulting team at Ascent.
What you’ll take away.
An understanding of the foundations of successful data transformations
How to remove culture as a potential barrier to success
A clear view of your path to readiness
Top tips on how to identify and deliver enduring business value
Rich Pugh
Chief Data Scientist
Ascent
As Chief Data Scientist at Ascent, Rich is passionate about delivering pragmatic advice to leading organisations on data-driven transformation and building successful data science teams.
With more than 20 years’ experience helping companies create value from data, Rich has worked across a variety of industries, helping businesses around the world increase profit margins, solve operational challenges and delight their customers.
Rich is a strong believer that there is nothing analytics can’t do and strives to help organisations leverage the power of their data.
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
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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.