How to leverage data analytics for architecture firms

Key quotes:

• "The role of the Data Architect is to understand the trade-offs between the different systems, their applications to the product/project, and the impact of introducing new components to the ecosystems and suggest the right course of action."
• "These can involve, business intelligence tooling, marketing automation tool, micro-services, customer and data management platforms, … In order to be handling the choice of these components, it is important for Data Architects to trial some of these components through proof of concepts and leveraging one’s experience to understand which solution would be more appropriate for both current and future requirements."
• "The architect should be responsible for designing the integration flow, whether the data needs to be provided to a message broker first, need to land on a staging table, or need to populate back the source systems."
• "Architects set forth the technical requirements, for instance in case of integration for instance, they provide the structure of the data feeds and define the integration pattern to use, in case of systems like APIs what’s the integration flow to handle."

Transcript: Everyone welcome to our session it's logical data warehousing for advanced analytics a little bit introduction about us I am Naga Prather attorney I go by pre attorney and I'm with IBM the Syl campus which is in South San Jose I'm the offering manager for db2 big sequel and my co-presenter is Herbert Pereira he's from Toronto and he's with the Canada labs and he's our chief architect so let's go into detail of the notices and disclaimers that's what we are here to listen about right just kidding so let me walk through quickly about what we mean by logical data warehousing so we are all the data work summit and it's all about data and data is like oil it's useless if it is crude so we do have to refine it and curate it to make it useful and once it's refined then you can you know have more analysis on customer behavior their buying patterns their sentiments so the businesses can offer a customized coupons or offers for them to benefit from so for the business to Bennett or no have this deep insights they need to have the whole enterprise data accessible and derive... See more →

Key quotes:

• "In most cases businesses need their old data in the new cloud data-warehouse."
• "Additionally, a continuous data loading might be required for integrating on-prem sources with the cloud data-warehouse."
• "Also here cloud vendors offer salvation by providing Data Scientists with things they need: notebooks, fast query engines and distributed computing capabilities."
• "But when it comes to distributed computing and complex data transformations or real-time ML model scoring, Data Scientists often need to write additional code using a specific framework like Apache Spark."
• "Google Cloud offers a solution called AI Platform where a Data Scientist can in fact get the power of distributed computing without having to know a respective framework like Apache Spark."
• "In our case, the Data Science team wanted to leverage the power of GCP’s Machine Learning solutions including AI Platform and BigQuery for preparing the data for training the ML models."

Key quotes:

• "Based on my experience, I’ve found the following principles to be critical to the success of an enterprise data program."
• "When starting with a persona-based approach, it is critical to build your next generation data platform by focusing on the people you’re looking to serve."
• "“Build it and they will come” isn’t a good strategy when it comes to data platforms, unless you’re highly in-tune with the end-user’s needs, and you have a way to mandate the tools they use."
• "However, you can craft your architecture to allow for flexibility of the data types you ingest and the ways you deliver information to each persona."
• "Here are a few things to consider when thinking about your architecture with flexibility in mind: This one is simple: Don’t be afraid of the cloud."
• "By using a persona-based approach, you can create security requirements in the early phases of development that meet the needs of all users."

Transcript: Hello everybody and thank you for joining us today for this presentation on transforming your business using big data and analytics today's session is specifically designed to address the needs of organizations in the transport and logistics industry my name is Sean Jackson I'm the European marketing director and it's my pleasure to be your host for today's session and I'm also joined by two speakers the first speaker is Richard stock who is our sales director here at action and biamby hopfield who is also a technical director at our partner Halton field now the agenda of today's session is pretty straightforward will first hear from Richard who's going to discuss the challenges and the opportunities in using data and analytics to make what we call transformational business outcomes in other words better business decisions based on your data and then we'll hear from Andy who will show you a demonstration a very very easy to use analytics and reporting solution that it's been specifically designed for industry users in sorry for transport and logistics companies around the world and then we're going to open up the webinar for questions and answers and to that point if you'd like... See more →

Transcript: - good afternoon maybe they're short introductions or my name is Guillaume from VP productors or company called Xenia you know vice-president that's a good thing about being part of a small company that you become a big tittles vice president happy to do that so Xenia we are building actually a smart data catalog so what we're going to talk about today is why you want you to have a data catalog in your in your on your company so the point is you are leverage your data set you know visits which is the promise of the new data world that you will join your company into that the central company in data centric organization where you can use the data to innovate new business to make better decision and the thing is it's not that easy to turn your company into what is now called a data democracy most of the time you have different attacks cultures and your maturity level your organization might not be to be that a democracy what's that a democracy it's an organization where many people everybody hopefully can actually use the data to make their decisions and to do some allergies... See more →

Key quotes:

• "For the purpose of data sources to be loaded into the data warehouse , there is need that the data should be well understood , normalized with the suitable data type definitions and in structured format.Although this kind of centralization enables security, backup, and failover of highly critical data, it also means that data typically must go through significant preprocessing and checkpoints before it can enter this sort of controlled environment, which does not lend itself to data exploration and iterative analytics."
• "As a result of this level of control on the EDW, additional local systems may emerge in the form of departmental warehouses and local data marts that business users create to accommodate their need for flexible analysis."

Transcript: The capabilities needed include the traditional things that people run to like technology hardware and software and what kind of applications but the truth is there's a lot of capabilities that have to be built around the organizational design people and processes because the truth is when you finish building your infrastructure and you've got big data and big analytics you've got to figure out who's going to use it how's it going to be used what kind of analysts are you're going to have do you need data scientists or just analysts you need a business solution architect or is it a simple database in addition to that how do you make sure that the data you get is good clean data you know the old garbage in garbage out still applies garbage in to a great model the model itself doesn't give great results so data hygiene data cleansing making sure data is clean and having data governance around who is responsible for keeping and securing clean accurate data that gets fed into the big data analytics becomes very very important so it's more than just the hardware infrastructure software and applications it's the people and the governance... See more →

Key quotes:

• "Big Data analytics use various methods and techniques such as natural language processing, machine learning, data mining, association pattern mining, behavioural analytics, predictive analytics, descriptive analytics, prescriptive analytics, diagnostic analytics."
• "As Big Data Architects, even though we don’t perform analytics, we still need to understand four major Big Data analytics types to create solutions in these areas."
• "For example, the use of linear regression techniques in machine learning and neural network to achieve the interdependencies of variables in captured data is commonly used for predictive analytics."
• "Predicting customer purchase goals by analysing their shopping behaviour is an everyday use case for Big Data solutions."
• "In addition, these four types of analytics, another trending analytics type for Big Data solutions is related to semantic technologies."
• "Semantics Data Analytics is an emerging and complicated process requiring various techniques and tools."