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The Data Engineering process is the result of technology disruption in what we used to call Big Data.
As the industry moves toward data management; environments that deliver insights from AI; and Machine Learning leveraging the cloud for agility; current technologies that we used in the past to use to manage big data won’t be big enough to tackle this evolutionary step.
Sysware makes appropriate data accessible and available to various data consumers, including data scientists, data analysts, business analytics and business users.
Our Data Engineers enable data users across an enterprise with clean, quality data they can trust, so they can drive better business insights and actions.
The toughest challenge for AI and advanced analytics is not AI - it’s actually data management at scale. But the scale of data has far exceeded the technologies that traditionally managed it.
Hadoop, MapReduce, Yarn, HDFS, are among the key technologies that enabled organisations to handle high volumes, wide varieties, and various types of data, i.e., big data. Compute, storage, and big data management were all closely tied together to drive data and analytics success from data lakes and data warehouses.
The adoption of cloud and advent of technologies have all ushered in the era of big data engineering, effectively uncoupling storage and compute, enabling faster processing of multi-latency petabyte-scale data with auto-scaling and auto-tuning. Cloud has been one of the biggest disruptors of big data – by separating storage and compute, by making it easy to scale and tune servers, and by bringing huge cost savings – in processing data engineering pipelines at scale.
Serverless: Serverless capability enables enterprises to build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster.
To understand the impact of data engineering on AI and analytics, let’s look at it from the vantage point of these data users.
- Lines of business (sales, finance, marketing, supply chain, etc.) need to answer key questions such as:
- How can data help me predict what will happen?
- How can data help me understand what has happened?
- How can my staff collaborate better and prepare data more easily?
Further, data scientists are spending 80% of their time in preparing the data, versus building the models; so, they’re asking:
- How will I find the right data for my modelling?
- How will I make this data available in my ML environment?
- How can I ensure I trust the data for my modelling?
- Can I simplify data prep so I can spend more time on modelling?
- How can I deploy and operationalise my ML models into production?
Similarly, data analysts do not have the right data for business insights to help drive actions, and they want to know:
- How will I find the right data for my business insights?
- How will I make this data available in my data lake?
- How can I ensure I trust the data?
- Can I simplify data prep so I can spend more time on analysis?
- How can I easily collaborate with my peers and IT for ongoing changes?
Data engineer to the rescue
Let our team help data scientists and data analysts find the right data, make it available in their environment, make sure the data is trusted and that sensitive data is masked, ensure they spend less time on data preparation, and operationalise data engineering pipelines.
- Discover the right dataset with an intelligent data catalogue
- Bring the right data into your data lake or ML environment with mass ingestion
- Operationalise your data pipelines with enterprise-class data integration
- Process real-time data at scale with AI-powered stream processing
- Desensitise confidential information with intelligent data masking
- Ensure trusted data is available for insights with intelligent data quality at scale
- Simplify data prep and enable collaboration with enterprise-class data preparation
Our Platform Engineering team of engineers and architects are focused on building flexible connectivity systems with robust operational foundations, that allow organisations to liberate the data tied up in backend systems to modern digital channels. We work with clients to embrace a platform-led approach to structuring and integrating legacy and emerging technologies more effectively and securely.
How we can help
We provide comprehensive product and platform engineering services that address the full technology stack throughout the entire product life cycle, delivering increased revenue and service levels.
Platform strategy & operations:
- Design & operate
- We outline strategies to define and maintain business processes for platform models.
Solutions we provide:
- Platform architecture Engineering for trust
- Business process for platforms Product management
- Build developer ecosystem Organise & run Platform Engineering
- Capability development
- We help to define custom capabilities needed to build and integrate with specific business processes.
- Build & augment datasets
- Geospatial engineering Responsible AI & AI transparency Advertising operations
- Payments infrastructure
- Establishing business models
- We help non-platforms to adopt platform models or services.
- Platform-based transformation
- Next-generation engineering
- Platform data design
- Micro-services design
- Business model design & refresh
At Sysware Group we take your information challenges and requirements and turn them into answers and insight. We use our experience, skills and technologies to design solutions that draw together the information you already collect, and turn it into the intelligence you need to make critical business decisions.
Our consultants can help you transform your data into valued information, enabling you to manage and use your data in meaningful and intelligent ways.
We apply BI technology and analytics to provide;
- Data warehouse design and development
- Reporting capability and development dashboards and cubes
- Data integration (ETL) development
- Data mining, segmentation and modelling
Sysware Group deliver solutions that at their heart retain business vision and context, but avoid using traditional monolithic, “big bang” approaches that have failed in the past and often incur significant resources and budget to implement.
At the start of each relationship, we sit down with key business owners from finance to marketing to ensure the business needs are met with the proposed solution and most importantly the BI solution is aligned to the organisations business imperatives. We then use an iterative methodology to regularly achieve milestones and progress very rapidly. Revisions and enhancements are incorporated quickly following industry best practice to dramatically improves your return on investment and let you start using your data faster.
Providing the Missing Link
Our goal is to provide the missing link that transforms your organisation’s information into meaningful, timely and reliable business information. And we present it to business managers and decision makers in ways that they can use to make informed decisions. We do this by engaging with the whole of the organisation, talking to key stakeholders about what they really need to know. This enables business unit owners and the leadership team to:
- Make well-founded business decisions
- Monitor and actively manage performance
- Make the links between day to day operations and overall business performance
- Accurately report to stakeholders
- Manage organisational risk
- Retain and build your customer-base
At Sysware, no matter where you are with your data science, we will support your journey from understanding the business needs and objective setting to generating powerful data visualisations and insight that inform, illuminate and support sound planning and strategy.
We build systems for data
Our statistical work is supported by excellent technical database and software development skills. We develop operational systems to allow organisations to get ongoing value from their data.
Make informed decisions
Our statistical analysis helps managers make the best use of available information.
- Get ongoing value from your data
- Build systems for data management, analysis, and display.
- Develop evidence-based decision and planning
- Ensure good policy, operational insight, consumer behaviour and other critical organisational data-based decision making is supported by quantitative analysis.