Big Data, A New Hope or the Same Old Menace?
Marcos Da Silva, Big Data Practice Lead
The emergence of Big Data tools and techniques promises to usher in a new age of better decisions; however, only those organizations that strive to become truly data driven will fully realize that promise. A data-driven organization is one where analytical insights drive decisions both tactically and strategically at a lower cost. The prospect of achieving this level of Information Management maturity can be daunting, and is accompanied by the same challenges facing any data-rich organization, including effective and efficient data governance, holistic tracking of metadata, enabling agility and productivity of the analysts who work in this space.
This presentation explores how to effectively harness the latest Big Data tools and inject your organization with a flexible approach to Big Data development and maintenance that will grow with your requirements and generate actionable insight to drive your business to the next level.
Contextual Omni-Channel Marketing
Tomas Synek, Campaign Management Practice Lead Adastra Slovakia
Knowing whom to target and when is a perennial problem faced by virtually all businesses, and has become even more critical as companies communicate with their customers across many channels. In this omni-channel contextual world, leading organizations seek to maintain a consistent customer experience across all channels, be aware of each channel's context, and make tailored 1:1 offers, which are often much more effective that mass campaigns.
This presentation explores Adastra's approach to presenting targeted, segment-of-one campaigns that leverage next-generation technology.
Advanced Data Visualization
Brian Cort, Advanced Visualization Expert
What good are advanced analytics if we don't understand what they are telling us, or how to incorporate them into our decision making process? Data visualization can bridge this gap, offering the possibility of increased performance and business value from the existing information underlying your business.
Effective data visualization improves both the speed and quality of decision making by allowing you to absorb more information, more quickly, to gain a deeper understanding of situations. It also enables you to communicate those results more effectively within your organization and to customers by presenting them in ways that both educate and persuade. Come learn how to identify opportunities where custom data visualization can add value to your business and see its benefits by exploring several successful solutions.
Industry Models on Hadoop
Nikola Raskovic, Lead Consultant Adastra
Big Data technologies have opened up a world of opportunities for business, promising to harness the value from the unstructured data at a noticeably lower cost of ownership. Parallel to the adoption of Big Data technologies, many large organizations are undertaking a major effort to integrate enterprise data around a single Enterprise Data Model (EDM), which should noticeably improve the value of the structured data as well as productivity. Implementing EDM on Hadoop should bring the best of both worlds by combining Hadoop’s lower cost with the higher productivity promised by integrated, structured, enterprise data, aligned to the EDM.
Unfortunately, Hadoop prefers unstructured data, and EDM is very rigidly structured. As a result, bringing Hadoop and EDM together, while avoiding crippling penalties in performance, requires an innovative approach. This presentation discusses the implementation of EDM on Hadoop, which should enable the conversion of Data Lake files into validated, integrated data, optimized for timely business consumption, and good for KPI calculations and regulatory reporting.
Due to constantly growing expectations from Business, IT must increasingly innovate to cover a wide range of application scenarios and enable a more agile approach. Many major automotive manufacturers are starting to use data lakes to exploit the additional potential of their data and to improve their market position through data-driven innovation. Special ‘labs’ were founded and made accountable to be the drivers of such innovations.
In this presentation, we look at the introduction of a data lake for the world biggest automotive manufacturer, from requirement analysis, to design and establishment of the Hadoop platform. The possibilities and challenges for data integration or the front-end decision are also sketched. The presentation concludes with a description of a use case on the basis of the data lake, which the department has implemented independently of the traditional DWH.
Native Apps for Better Analytics
Standa Dvorak, CEO Adastra.One
Most organizations seeking to leverage advanced analytics turn to standard-use prepared frameworks from the main Business Intelligence vendors. However, such frameworks offer a once-size-fits-all approach in response to a challenge that is highly complex and multi-layered. Native apps developed specifically for your platform, and for the way your users engage with it, are much more sophisticated and streamlined. This presentation will explore what native apps can do with analytics, with a focus on a use Adastra.One developed for a leading social media solution provider, Social Bakers.
As data management and analytics technologies improve, leading organizations are employing predictive analytics and machine learning to drive strategic business decision making. From churn prevention strategies to next-best offers, advanced analytics are paving the way to predicting, protecting, and growing your revenue. As a pivotal part of predictive analytics, revenue forecasting allows organizations to make better planning and investment decisions.
However, to be effective, solutions need to be implemented correctly and answer the right questions. John Yawney, Adastra's Data Science Lead, and Rob Turner, Adastra’s VP of Big Data & Analytics give an overview of some predictive analytics approaches and discuss methods that can be applied to revenue forecasting.
Semantic Analytics – The Art of Analyzing Relationships
Stefan Yordanov, Data Science Lead, Adastra Bulgaria
Successful organizations derive value from their data by analyzing it through multiple lenses; however, many analytic efforts are hobbled, not by a lack of data, but by a surplus of data in a wide variety of incompatible data sources. As the real-time demands on analytics increases, business users can only be productive if they understand complex database and data science models.
Semantic analytics helps organizations overcome these roadblocks by adding a knowledge ontology layer to data, which enables even non-specialist users to deliver compelling insights from diverse data. Using social network analysis and semantic web technologies, users easily uncover direct and hidden relationships, paths, and patterns between people, events, and objects. Join us as we review the advantages of semantic analytics over analytics based on traditional relational databases, and explore how linked data can be used to enrich datasets and derive new knowledge, both from a methodological perspective, and via an insurance fraud-detection case study.
Standing the Test of Time: Big Data Metadata and Automation
Nelio Lucas, SVP Technology, Chief Architect & Chief Information Security Officer, Adastra
In the wake of the Big Data revolution, the prospect of replacing legacy analytical platforms with new, high-performance ones is tantalizing. Leaders often insist that their new and innovative platforms be rapidly set up and prove their worth with quick-wins to justify continued expenditure. However, the focus on quick results risks reducing the useful lifespan of the solution.
While a robust architecture is essential, long-term success is only possible if data and processes can be holistically understood; in other words, a solid metadata foundation is imperative. Low cost, mature metadata solutions are now available and can be implemented expediently to manage metadata in Big Data environments.
Agility and responsiveness is vital if insight is to be delivered in time to impact innovation and effectiveness. The teams that support and maintain the platform must be flexible enough to set up analytical sandboxes and extract insight in a matter of hours, not days.
This presentation will examine the current state of Metadata Management solutions for Big Data platforms and how their quick and phased implementation can support better adoption and usage of the platform.
Additionally, we will also see how requests for new Analytical Sandboxes can be streamlined and the whole process automated, providing users with the required flexibility and simultaneously, allowing the support team to focus their effort on other valuable tasks.
Tackle Your Data Lake challenges Using MDM
Michal Klaus, Ataccama CEO
Data Lakes are a new concept that is gaining significant traction. Ever-expanding data volumes and a growing number of data formats and input streams are now simply a way of life for most mid-size and large organizations.
They provide pure processing power and scalability, highly favorable price-to-performance ratio, and unprecedented capacity. These opportunities translate to business benefits such as dramatically shortened go-to-market times for new products, individual approach to customers or micro segments, the ability to build customer and risk profiles from relevant data sources, and much more.
This session will address new challenges brought by Data Lakes, how MDM can help, as well as examine new and exciting technologies and concepts for traditional MDM.
What is Internet of Things and should I care about it? Can I use it in my line of business? Is it for some select businesses only? This session will discuss widespread IoT applications, their architecture, and the challenges facing their implementation in various industries. We will also look at an actual IoT solution and an integrated IoT command center that manages the devices across multiple buildings and sites.
Natural Language Processing has been a notoriously difficult challenge for leading analytical organizations. Simply put, human speech and writing styles are so varied and nuanced that deriving value from text data without human interaction still presents a significant challenge.
Adastra has been helping some of Canada’s leading organizations navigate these waters, and we present some practical steps to start you on your Text Analytics journey.