Displaying search results for ""

DataOps : The new DevOps for Analytics

DataOps : The new DevOps for Analytics

Understanding the similarity and differences between DataOps and DevOps; and the relevance of DataOps in present day Banking

DataOps, while often spoken as the ‘new DevOps for Analytics’ is a collaborative data management practice focused on communication, integration and automation of data flows across an organization.

DevOps and DataOps leverage different organizational people and their expectations. DevOps serves software developers who embrace complex details of code creation, integration and deployment. DataOps users on the other hand, are usually data scientists and analysts focussed on building and deploying models and visualizations. The DataOps mindset focuses on domain expertise, and is interested in getting models to be more predictive or deciding how best to visually render data.

Where by using DevOps (Continuous Integration and Continuous Delivery) leading companies (Amazon, Google, Netflix, Facebook, Apple) have accelerated their software build lifecycle (earlier called ‘release engineering’) to reduce deployment time, decrease time to market, minimize defects, and shorten time required to resolve issues; DataOps seeks to reduce the end to end cycle time of Data Analytics – from idea origination to value creation through charts, graphs and models.

The Similarities between DataOps and DevOps

DevOps and DataOps both rely on similar architectural principles (cloud delivery) for Continuous Integration and Delivery, as also they harvest cross-team (development, operations, analysts, architects, scientists, quality monitoring and customers) collaborative energies that drive value creation and innovation.

In fact as modern organizational cultures promote ‘Data Literacy’, newer approaches (like self-service data preparation tools) come equipped with their own in-built data operations. As a result, today data practitioners not only collaborate and co-develop insights in a zero-code environment, but also streamline work delivery across the organization.

Another significant end purpose that unites the two is large scale global consumption and provisioning. As DevOps and DataOps function in high-speeds, multi-geography scenarios whilst accommodating lots of users, both need a unified management environment, where monitoring, and cataloguing can happen concurrently.

The ‘Factory-model’ in DataOps

Whereas Agile and DevOps relate to analytics development and deployment, data analytics additionally manages and orchestrates a data pipeline. Data continuously enters on one side of the pipeline, progresses through a series of steps and exits in the form of reports, models and views. The data pipeline is the ‘operations’ side of data analytics and is called ‘data factory’, just like in a manufacturing line where quality, efficiency, constraints and uptime need to be managed.

The Intellectual heritage of Data Ops

DataOps combines agile philosophies, DevOps principles, and Statistical process control (a lean engineering tool) in balancing four critical Data aspects, namely, engineering, integration, security and quality – to uniquely manage an enterprise-critical data operations pipeline.

Andy Palmer

Banking and DataOps

Broadly speaking, DataOps targets improving data and analytics quality, reducing cycle times for creating new analytics, and increasing the productivity of the data organization exponentially.

More so, Banks have an additional criticality in adopting DataOps powered infrastructure: that of preventing (and bouncing back) from system breaches, platform outages, and process malfunctions that inevitably erode consumer experience and trust.

As DataOps powered platforms start to play a more pivotal role, banking organizations will realise that data needed to fuel innovation cannot be left siloed in legacy applications. Accurate data for precise decisions at the exact speeds will not only combat bottlenecks and outages but DataOps platforms that combine on-demand data delivery with data compliance will boost Customer experience transformation and innovation – especially as  more customers shift to online and mobile banking channels in the current pandemic crisis.

View

Top 3 Technology Focal Areas in Banking in 2021

Top 3 Technology Focal Areas in Banking in 2021

It is crystal gazing time and as COVID pandemic would indicate yearly predictions can be a risky exercise. 2020 saw forced changes across Industries and geographies. For cost optimizations retail banks embraced intelligent and leaner operational models by leveraging open ecosystems. Cloud migrations with their advantages of security, data analytics, and storage assisted banks to pursue digital transformations, revamp business models and accelerate innovation capabilities. Additionally, Banking as a Service (BaaS) is enabling banks to monetize their data, services and infrastructure as consumable API’s for third parties, and co-create new products with faster time to market.

Along with the megatrends that COVID accelerated, namely Digitization, workplace virtualization, Safety surveillance, Cost reduction focus and new-age ecosystems; here is a round-up of the top three technology focus areas in the Banking sector.

  • Use of AI and Data for Hyper-personalization: Micro-segmenting as a digital strategy will underpin the shift from mass production to mass personalization. Banks that treat customers as segments by themselves will gain loyalty, increase lifetime value and reduce churn. 2021 will see banks ramping their data capabilities and advanced analytics to tailor real time financial recommendations. Surgical precision will permeate hyper-relevant content, hyper-specialized products, customized pricing.
  • Green Banking: Expect this year to be an inflection point banks to focus on managing climate risks, adopting green operations, and developing green products. From motivating customers to opt for zero emission vehicles with clean-energy loans to zero processing fees on electric vehicles, to manufacturing cards from recycled plastic, and creating a corporate position for a chief sustainability officer; corporations world over are pursuing green banking to counter environmental and climate risks by incorporating them into their corporate governance.
  • Voice Technologies and Autonomous finance: Technology trends, often, are driven by Gen Z habits. Present day voice assistants tell us everything from weather forecasts, stock market data, plays songs or offer road directions. In 2021, expect voice technologies to play a larger role in autonomous finance. They will reshape the way people interact with money and its service choices. From basic information, and account services, to using voice as one of biometric data security features, the ways of autonomous finance to power customer convenience will permeate daily banking processes. Banks will make serious progress with AI and ML managing user money across the vast portfolio of products and services.

In summary, 2021 will be a year of consolidation of all that the previous year accelerated – Digital banking, E Commerce, Contactless payments, AI advances in security etc.

In fact, as the year progresses, expect financial organizations to consolidate gains that have come from: Redefining the future of workforces and workplaces, humanizing digital experiences, and the neo-normal benchmarks set for Customer centricity.

View

Evolution Of The Data Analytics Industry

Evolution Of The Data Analytics Industry

As early as the year 1848 scientists discovered that the “Prefrontal Cortex” of the brain is a small area that defines the personality of an Individual. Later studies have shown that this area is responsible for higher order data processing, decision making and executive functions of human beings.

Drawing a loose analogy one could argue that the Data, Analytics function performs that role within an Organization. As the modern corporation has evolved, so has the “Prefrontal Cortex” over the last 30 years. And one would argue that this evolution and development will gather pace as we move ahead.

This piece examines this evolution over the last thirty years.

Analytics 1.0: The era of ‘Business Intelligence’

Organizations have always recorded, aggregated and analysed data about production processes, sales, and customer interactions. Data sets were small and stable in velocity to allow for segregation in data warehouse for analysis. However, more time was spent in preparing data for analysis and relatively little time on the analysis itself – which was painstakingly slow often taking weeks to perform.

At the start of the new millennium modelling of data for analytics received a boost thanks to Ralph Kimball and Bill Inmon who did some pioneering work. Analytics stepped into mainstream when the relational database came of age. Technology vendors came out with products like IBM DB2, Oracle V3, Sybase (SAP) and the first standardized SQL based decision support systems went live. Still most analytics efforts were focussed on Descriptive and Diagnostic outcomes. This era lasted till the early to middle of the millennium.

Internet goes Global: Enter Analytics 2.0

Amazon (1995), Hotmail (1996), PayPal (1998), Google (1998)

Early and Mid 2000s businesses recognised the need for powerful new tools to get ahead in the market. Many technology Innovator Companies sought ‘first mover’ advantage with accelerated new products – OLAP , Reporting, Data Mining and ETL. This led to the emergence of specialist tool vendors like Informatica ,Business Objects and SAS.

In mid to late 2000’s organizations shifted away from pure RDBMS to MPP (massively parallel processing), specialized toolsets, and advanced analytics – all in recognition of ‘DATA as a critical asset’. And data volumes grew dramatically as did the cost of storage and processing.

Analytics 3.0 starts as the World goes Social

LinkedIn (2003), Skype (2003), Facebook (2004), Twitter (2006)

In this age Web apps went into a hyper growth mode. More events, more users, more transactions and the start of the smart phone and connected era.

Technology players responded with massive multi-rack systems, 100’s of computing cores, and Terabytes of Storage. Distributed computing, advanced query plans, columnar data models and Re-programmable hardware. Major players created a new wave of MPP OLAP’s (Online Analytical Processing) platforms – Vertica (HP), Greenplum (Pivotal), Netezza (IBM), and Exadata (Oracle).

But soon organizations realise that Big Data could not fit or be analysed fast enough on a single server this led to the move to Hadoop and distributed parallel processing. To deal with relatively unstructured data, companies turned to a new class of databases known as NoSQL. New technologies – ‘In memory’ and ‘In database’ analytics were introduced for faster processing. Machine learning models started to be used for advanced analytics. The world of bland boring reports gave way to compelling and intuitive visualizations.

This era continues into the late 2000’s and early ‘10s, coinciding with the rise of the ‘Data Scientists’, the Open source revolution, Fast Data, API’s and IoT’s.

In 2013, it is recorded that WhatsApp in a day sends 31 billion messages and 700 million photos sent. These are unimaginably large data volumes and growing!

Analytics 4.0 : “Fast-Pervasive Data” is replacing “Big Data”

The next generation data scientists used both computational and analytical skills and business context to solve various problems. Analytics got embedded into decision and operational processes. As technology continues to push further – Streaming and Real time analytics is made possible by Apache Spark, Kafka among Open Source platforms. Today their new avatars are available on the leading cloud platforms enabling massive Streaming and complex analytics.

In summary much like the way Human Intelligence (Prefrontal Cortex) drove rapid progress, the Data, Analytics function is driving competitive edge in the world of business. The onus is on CxOs to drive the rapid evolution of data and analytics and integrate it into key business processes.

But one factor decision makers need to understand is the pace of change in Data Analytics technology. And how the right choices could be a major competitive differentiator for their business. As Theodore Roosevelt said “The more you know about the past, the better prepared you are for the future.

This was originally published on Business World website and is being reproduced here.

View

5 Marketing Mantras to accelerate in the New World Order

5 Marketing Mantras to accelerate in the New World Order

The pandemic has brought about unprecedented business impact across all sectors. Human interaction and behaviour is no longer predictable as the new world order has taken over. In such volatile times, marketers need to continuously innovate to keep their audience engaged.

Change was the only constant in the year gone by. The below points highlight key trends that will bring about systemic changes in the way marketing is going to be done in the future.

  • Digital – The Great Leveller: Going digital for marketers was no longer a choice. Since everyone was working remotely, marketeers had to find new ways on engaging with their audience, without any human touch. This meant that the winner was not the one with the highest dollar spend but the one who could break the clutter. Small and Medium Enterprises were more nimble and grasped this opportunity with both hands as they could now compete with the big names on a level playing field. This trend is likely to continue and drive new ways of engagement through the digital channels. This means that we will see a lot more innovation in the adoption and usage social media, SEO, email marketing, virtual events to name a few.
  • Efficacy and Efficiency Drive Marketing Technology Investment: Given the state of business, marketing budgets were also slashed across the board. Suddenly, RoI became paramount for marketing leaders. It also made them realize that technology adoption can achieve a lot more with a lot less. Investments in automation tools, chatbots, AI-powered and data-driven marketing became real. This trend will only get stronger post pandemic as marketing teams will continue to gain from revisiting their marketing technology investments.
  • Personalization will be the key: As customers started to become more careful with their spends, marketers realized they had to make an emotional connect in order to drive any message across. This meant fewer but highly personalized messages across multiple channels synchronized in a way that they feel compelled by a need. Thus, the use of technology backed by limited engagement options made marketeers focus on personalization a lot more. The trend which was more in talks is now a reality.  Further, Account based marketing will also see hyper-personalization as messaging becomes more important and marketing teams become more tech-savvy. The quality of interaction enhanced by higher customer intelligence will make the overall experience much richer.
  • Empathy will continue to be the key theme: The world has gone through a lot of pain. People have lost their loved ones, their jobs and all their life’s savings. A big change observed in corporate behaviour was that of empathy. This also reflected in the messaging that was used across customer channels. It was a much needed change from the ruthless selling that was being done in the past. Post-pandemic too, this theme is likely to continue as we rise from the ashes and move towards a world which is value oriented rather than ostentatious.
  • Content All The Way: With efficacy, efficiency and personalization being the focus, it all came down to content. Execution at content level was monitored across all management levels as companies realized the need for quality rather than quantity. Going forward, Content marketing — creating useful, relevant and credible content that benefits the user — will remain the core for marketers. Starting with the content or the message that speaks to our audience, then translating it to the relevant channels and formats, will be the only way to create consistent, effective user experiences. Moreover, interactive & visual content such as assessments, games, polls, interactive videos and surveys will become more prevalent as communication will change to bi-directional.

    In summary, in these times marketeers should :

    • Be nimble. Plan but be ready to change it.
    • Invest in technology as that will give you the edge.
    • Analyse buyer journeys before making investments.
    • Focus on visually compelling, interactive & quality content.
    • Lastly, bring genuineness to every moment.
View

Identify Help Zones through Mind maps, Push your Boundary and Discover the ‘Queen’

Identify Help Zones through Mind maps, Push your Boundary and Discover the ‘Queen’

Oftentimes it is not the change itself that poses a challenge as much as our inability to seek help.  Whatever the change may be – ranging from as simple as moving into a different workspace within the same office to joining a new company – we may be reinventing the wheel by not asking for help!

H-E-L-P. It is a four-letter word – innocuous, universal, and a sign of our being human. And yet, sometimes the hardest thing in the world is to ask for it.  Asking for help is a scientific process and when mastered can accelerate learning and therefore, productivity.

In recent times, however, the waves of hashtag awareness campaigns on the social-media-seas, is thankfully reversing our attitude towards seeking help. We may not have turned into proactive-help-seekers overnight, but the criticality to do so, increases each day. We miss the interactions that characterized the pre-pandemic days, etching in our consciousness, forlorn memories of the cafeteria conversations, passionate debates, impassioned debriefs, celebratory events, and not to forget the heavily loaded smiles that we exchanged with our prank-mates even while rushing to take the crowded lift car so we may make it on time to office!

We are getting used to the new normal and learning cannot happen through real-time observations and classroom spaces alone, not anymore!

New situations call for new learning methods. It is time to seek help by asking the right questions and learning thus.

Inclusive leaders understand asking for help is an organic way of allowing team members to share their expertise and knowledge with each other. Getting out of comfort zones, is the first (and the toughest) step towards beating the forces of ‘negative’ gravity and ‘fear-of-failure’ inertia.

For those of you who are new to mind maps, here’s one coming your way… and yes, do watch the Queen’s Gambit, if you already have not. The power of the mind can bring to life even the boring ceilings of a dilapidated orphanage!

Mind Maps

And, the next time when someone pushes you, understand that they see your potential and want you to realize your inner power.

When you encounter an unknown terrain, start with tracing out a Mind Map. Mark out areas that you need help executing or even formulating. Articulate this to your team. You would have pushed your boundaries, acquiring the power of the proverbial queen!

View