What are the 5 best reasons to attend Informatica World 2017? I knew you were wondering and I’m glad youRead more…
What are the 5 best reasons to attend Informatica World 2017? I knew you were wondering and I’m glad you asked! Of course, this list will be a little different for each person you approach, but I’m happy to share my opinion here.
Most would predict that I’d answer with things like “visiting San Francisco”, which truly is one of the most beautiful and interesting cities in the world. Or perhaps I’d answer with the obvious: The spectacular opportunity to hear from thought leaders like Michael Lewis and Kara Swisher. I couldn’t be more excited about both! But, from my perspective, the single very best reasons to attend are the opportunities to personally grow. By improving your own data management skills, you stand to gain a competitive advantage in a time of increasing complexity.
Data is well on the way to becoming the most strategic asset an organization holds. The staggering growth rates of data can’t be ignored. The gathering, management and usage of data is now crucial for nearly every business decision. With that in mind, here are five key reasons for you to join us at Moscone Center on May 15:
1) Grow Your Cloud Data Management Skills
This year’s event will include an Industry Perspective on Cloud Integration and Hybrid Data Integration. In this session, Gartner and others will share ways to navigate your Journey to Cloud successfully. You’ll hear directly from Informatica Cloud customers about their best practices and potential pitfalls. Throughout the week, you’ll also hear peers discuss new best practices in cloud data warehousing, cloud analytics and SaaS integration.
2) Grow Your Big Data Management Skills
This year’s conference also includes an Industry Perspective on modern Data Integration and Big Data. In this session, you’ll hear from experts at TDWI Research, Deloitte and Cognizant. Together, they’ll share new ways to use state-of-the-art data management to quickly and systematically deliver of accurate and consistent big data insights to their organizations. In addition, throughout Informatica World 2017, you’ll learn new ways to deliver faster, easier, and more repeatable data integration in any environment.
3) Grow Your Master Data Management Skills
The opening day of the event also includes an Industry Perspective on Master Data Management. In this session, Constellation Research will share how cloud and social media have made Master Data Management essential to organization survival. During the session, you’ll learn specific ways to use MDM to turn big data into big insights. And, throughout the week, you’ll hear examples of partners and customers who’ve used MDM to improve customer experience, optimize business processes and ensure regulatory compliance.
4) Grow Your Data Quality Skills
During Informatica World, you’ll learn ways to ensure that your data is consistent and trustworthy. Throughout the event, you’ll have numerous opportunities to learn from customers who use Data Governance, Data Quality and Data as a Service to meet their business needs. We’ll also reveal new, game-changing ways to deliver enterprise Data Governance. You’ll learn how Informatica Axon can help you deliver reliable data for analytics and compliance.
5) Grow Your Data Security Skills
Over the course of Informatica World 2017, you’ll get a close look at implementations that enable organizations to protect their data in their diverse environments. Throughout the event, you’ll learn specific ways to reduce the risk of data breaches, improve compliance and prioritize security investments. You’ll hear directly from customers and partners who have used data security intelligence and data security controls to increase enterprise architecture security, despite increasingly diverse environments.
So What are YOUR Best 5 Reasons?
Now you know my top 5. I see this as a “can’t miss” opportunity for personal growth. In fact, with a full conference pass to Informatica World 2017, you’ll have access to the general sessions and breakouts, as well as the deep dives and round tables. In addition, if you register by March 31, you’ll receive priority access to the Session Scheduler which will allow you to reserve a seat in the sessions that matter most to you, BEFORE they fill up. Thanks for your consideration, and I truly hope to see you in San Francisco!
The post 5 Best Reasons to Attend Informatica World 2017 appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.
Source: Informatica Perspectives
Moving from digital transformation blabla, to actionable steps, this is one of the key objectives for Informatica’s MDM 360 SummitRead more…
Moving from digital transformation blabla, to actionable steps, this is one of the key objectives for Informatica’s MDM 360 Summit EMEA on June 7 and 8, in Amsterdam Arena. Where do you stand in the in digital? World Cup, Champions League, first league, second league, amateur? How do you rank compared to competition? Do you have the right team together? Does everybody know which role to play?
Like in football, it is never too late to change the game. “The ball is round, so that the game can change direction”, a football legend once said. In business, data defines and changes the direction of the game. Data defines, who will be winners of the digital championship, while looser will be left behind. I know my name in Dutch means cow or bull, in German it actually means round. So there is a lot potential for jokes around my name, especially as a football fan.
I am thrilled to invite you to join me in Amsterdam this year. As a football lover I am looking forward to explore the stadium and the stories behind the scenes. I am even more thrilled to have a great line-up of digital thought leaders who grant you a look behind their scenes and will be sharing how they use data to streamline processes, boost customer experience and ensure compliance in regulated markets.
5 reasons why you cannot miss MDM 360 Summit – sneak preview of a fabulous agenda
- Learning from best in class examples: A leading analyst recently told that he is impressed how many customer use cases we always bring to this global event series. That was a big compliment and continues to be our singular focus. We grant you access to leaders in business and IT to learn from their do’s and don’ts.
- Customer Experience: “It is not about the data, it is about the customer”, said Informatica CEO Anil Chakravarthy. During our MDM 360 Summit in NYC in February we launched the all new Customer 360 and Relate 360 solution, now in Amsterdam we will even take this one level further! Our own Chief Product Officer & EVP, Amit Walia, will unveil great things on stage.
- Digital Transformation: Companies from different Industries will share the very best secret: how they are using data, to change the game and transformed their company.
- Journey to Cloud: This breakout is tailored for the CIO and his staff, executing on the journey to cloud. Which data stays in premise, which data can live the cloud? Our solution experts will expose you to best practices.
- Data Governance: A holistic approach for data governance is fundamental to a data driven business, while the raise of new regulations, such as GDPR, are bringing new challenges to you. Informatica will enable you to change the game in data governance, will the all new and industry’s first enterprise data governance app.
Great speakers, from great brands
Your will have direct access to leading CIOs, CDOs, market & eCommerce leaders and many more to hear their best practices. Check out the detailed agenda here. We are adding more great stories for you every week.
The world’s largest MDM practitioner community comes to Amsterdam. Informatica was ranked as the MDM vendor with the largest active group of virtual and on site community members. The best thing is: You can be a part of it. Register now.
Our certified implementation partners play a key role in your success
I am thankful for our great community of MDM partners, who will be sponsoring this event and sharing the deep expertise with you during the sessions and our exhibition floor. More than 2000 partner consultants are certified for Informatica solutions turning your business case into real business outcomes.
Join us and change your game! #MDM360
Source: Informatica Perspectives
In Part 1 of this series, we began with the roots of Metadata Management. In this post, we look atRead more…
Big Data’s close relation to Metadata Management
Now is the new era of Big Data. This includes the next-generation capabilities of various areas including machine learning, predictive analytics and statistical analysis. However, these very powerful big data processing and storage capabilities that are continually changing. And the central underlying technology is that of Metadata. Having a universal metadata catalog gives us this transparency, this view of how the integration is done.
Also, the Hadoop ecosystem is constantly evolving and most of the platforms do not have any descriptive information of their data. Therefore, the data needs to be discovered and relationships need to be inferred rather than ‘presented’ readily to the users. This use case highlights the key need to have a Catalog as part of Metadata Management which collects data assets across the enterprise.
360-degree relationship discovery
There is a necessity to have a 360-degree view of data to easily search, discover, and understand enterprise data and meaningful data relationships. Discovery process also involves the finding of related data sets, technical, business, semantic and usage-based relationships. Below are the commonly encountered use cases:
- Finding relevant data sets with powerful semantic search capabilities
- Discovery of sensitive elements within the big data landscape
- The need to discover similar data assets
Metadata Logical Architecture: Data Flow
The below diagram illustrates the logical metadata architecture where data flows all the way from various sources all the way through to the BI layer:
The cataloging solution is not just spread across the data lake but across the entire enterprise. In short, the entire backbone of the enterprise data management rests in the metadata warehouse. Therefore, the need to discover the data and perform powerful semantic searches rests on the core architecture which is metadata-driven. As the Hadoop ecosystem continues to evolve, we believe that implementing a metadata-driven approach for Big Data initiatives will result in more value, credibility and ultimately the better understanding of data relationships in your enterprise.
In the next part of the series, we will take a detailed look at how “search” plays a key component in looking for data assets in the catalog.
The post The Next Generation of Metadata Management (Part 2) appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.
Source: Informatica Perspectives
Anyone can be a victim of identity fraud, whatever your age and status – and as identity fraud reached recordRead more…
New statistics from fraud prevention organisation CIFAS has found that the overall number of recorded cases of Identity Fraud in the UK in 2016 were almost 173,000, an all-time high level and a 59% increase on the figures just three years earlier.
What’s more, almost 25,000 victims of fraud were aged under 30, and the number of under-21s affected was up by a third.
Did you know that almost nine out of ten (88%) identity frauds are now committed online? With so much personal data on the internet, we are all potentially vulnerable to hacking or phishing. Your full name, date of birth, current address and national insurance number, and the passwords and PINs to your bank accounts are among the things fraudsters are hoping to get hold of.
Once identity fraudsters have enough of your personal details, they can apply for credit in your name and run up debts without you knowing. In fact, you’re 17 times more likely to suffer a case of fraud than a robbery.*
What you can do to protect yourself
- Check your social media privacy settings and don’t give away too much personal information
- Try not to write down or list PINs or passwords, in emails too
- Keep an eye on post you’re expecting, and redirect post if you move home
- When shopping online, it’s best to use websites that you know and trust.
- Always look for a security padlock icon in the top left hand corner of a page before you register financial or personal information on a website.
What are the fraudsters doing?
CIFAS say that the vast majority of identity fraud are when a fraudster pretends to be an individual, attempting to buy a product or take out a loan in their name. They may try to use:
- So-called ‘phishing’ emails or phone calls pretending to be from your bank
- Setting up fake banking or credit card websites to get you type in your details
- ‘Mining’ your postings on social networking sites such as Facebook for information
- Scanning the ‘dark web’ for details of lost or stolen cards
How might you suspect identity fraud?
Often the first time you notice that you’ve been a victim of identity fraud will be when you try to apply for credit and are turned down because of your level of debts. Other red flags can be:
- Charges you don’t recognise appear on your statements
- You receive calls or letters from collection agencies
- Bills arrive from companies you haven’t dealt with
- Your credit report contains accounts that you didn’t open, or applications for credit that you haven’t made.
If you suspect you’ve been a victim of ID Fraud
To report fraud, attempted fraud or cyber crime , the first step is to contact Action Fraud – the UK’s national fraud and internet crime reporting centre – and receive a police crime reference number. They will advise on the next steps to take and tell you any other organisations to contact.
If you are concerned that one or other of your online accounts has been compromised, then it is worth changing your password(s) to a new one as soon as you are able, and try not to use the same passwords for different accounts, especially those with financial information.
*ONS overview of fraud statistics (July 2016)
In Data We Trust There’s only one measure of the success of a data-driven organization, one which can make itRead more…
There’s only one measure of the success of a data-driven organization, one which can make it break it – and that is trust.
Trust is the glue that holds enterprises together. It is the asset that will never age. It is the commodity that will never be commoditized. If decision-makers trust the data they are receiving, you are truly on the way to becoming data driven.
Unfortunately, there is still a sizeable trust gap within today’s enterprises, a recent survey finds, especially when it comes to Data Quality. On average, organizations around the globe believe that 27 percent of their current customer and prospect data is inaccurate.
That’s the word from the Experian Data Quality benchmark report, which surveyed more than 1,400 executives across the globe. While the report author is in the data quality business (and therefore, has a horse in this race) it’s telling that so many executives admitted to having reservations about their organizations’ data.
At this point, less than half of the executives (44%) trust their data enough to make important business decisions, the survey shows. A majority, 52%, still rely on gut feelings, and same percentage even admit that a lack of confidence “in data contributes to an increased threat of non-compliance and regulatory penalties, and consequently, a downturn in customer loyalty.”
The higher you go in the organization, the more skepticism you will find about the validity of data. The aforementioned study found that C-level executives have a higher degree of distrust in their data than those in other roles. On average, they believe that 33 percent of their organizations’ data is inaccurate. At the same time, lack of support from upper management holds back data quality improvement efforts, the survey report’s authors observe. “We believe that although senior leadership conceptually understands the value of good data, the lack of a solid data strategy delays executives from making long-term investments in that area.”
There is good reason to move forward with data quality efforts.
The majority of organizations globally say that they have seen benefits across many areas of their businesses, including increased revenues, employee efficiency, and improved personalization and targeted marketing. For starters, 85% of enterprises said they saw more timely and personalized customer communications as a result of improving data quality . Another 83% say they have seen some improvement in employee efficiency after implementing a data quality solution, and 82% say that they have seen some progress when it comes to revenue growth.
The post In Data We Trust (Most of the Time) appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.
Source: Informatica Perspectives
Business users always had an insatiable hunger for data. They were already discovering and mashing up data from different sources, aggregatingRead more…
Business users always had an insatiable hunger for data. They were already discovering and mashing up data from different sources, aggregating them in excel and presenting PowerPoint analysis in the 1990s, albeit with difficulty. Then technology caught up. Self-service Business Intelligence tools like Tableau, Qlikview and more recently Amazon QuickSight made the aggregating, visualizing and sharing of data easier. The claim was zero to analytics expert in minutes! Overnight business users became data heroes. They also started seeing IT which had for years prepared and governed use of data in enterprises, either unnecessary or worse, an impediment on their journey to insight.
However, as many organizations are realizing now, this new world came with newer problems:
Correlation, Causation and Coverage: With Self Service BI tools, organizations expected business users to find causes for major past issues, predict market situations better in the future and take better decisions today. However, the path to insight is paved with obstacles. Most analysis do not factor in all data even when it is available in the enterprise. Even when business users knew that a particular attribute can make an analysis better, they did not use it because it is impossible to know where to find that attribute or even if that attribute was recorded/available to them. This resulted in shortcuts, uncorrelated and incomplete analysis, finally leading to incorrect decisions.
Bad data=Bad Analysis: Business users went through all the work of discovering data sources, creating elaborate reports and painstakingly generating new insights, only to realize that data they used was obsolete or came from a wrong source or had serious data quality issues or was used in an unintended way without understanding of business context (Should Customer Acquisition Cost be averaged daily, monthly or yearly? What should be the value of customer lifetime in calculating Customer Life time value? Should I count only closed deals or commits while assessing the sales force efficacy for a quarter?). Add behavioral issues like “seeing what you want to see” or “pointing data fingers at anyone other than me”, organizations are slowing realizing the unreliability of user created reports and dashboards for taking any meaningful decisions.
(Re)running the preparation wheel: Self Service Data Preparation and BI also resulted in a large number of business users performing the same preparation, standardization and reconciliation tasks again and again for the same (raw) datasets. So while the individual wait for getting access to raw data reduced, the whole organization paid multi-fold by repeating the same tasks.
How can a data catalog help?
It is clear that along with self-service BI and data preparation tools, a data “superhero” needs the following additional capabilities to be truly effective:
Search: Ability to quickly find data relevant to analysis needs is essential to derive value from data. This search should also lead user to get business and usage context for the data as well: who has used the same datasets in the past? For what kinds of analysis? How was it transformed? What are the other related datasets?
These users really need a “google for enterprise data assets”.
Informatica’s Enterprise Information Catalog can automatically scan and index metadata from most data and application sources in the enterprise. Once indexed business metadata from the enterprise business glossary can be associated with technical assets which can help business users search for these datasets using business terms instead of technical jargon. The system understands synonyms and known variations of the same term, to deliver intelligent search results. Enterprise Information Catalog also scans data movement and data preparation sources, cataloging and indexing recipes and mappings that can help with reuse of work in transforming data.
Establish Trust: Ability to trust the discovered data asset is important as well. What is the data quality of the dataset? Was it used in external reporting (can be reasonably trusted)? Is this the trusted source of customer segmentation data when I am doing segmentation analysis?
Enterprise Information Catalog extracts both data quality statistics and data lineage relationships to help users with establishing trust and relevance to their analysis needs. It also allows users like data consumers, data stewards and data owners to add additional metadata like comments and tags to help distinguish good data assets from time sinks.
Classifications: Ability to classify datasets for better management is essential as well. Classifications can be across multiple dimensions like data ownership or geographical locations, or the semantic label of contained data or something else. These classifications are the first step in governing, managing and extracting value from data. However, for enterprise size data classification problems, we also need platforms that can scale. If the system depends on humans to classify all data assets manually, it will take an eternity to classify all data. If it performs all the classifications automatically, all the human time will go in cleaning up false positives. Enterprise Information Catalog uses machine learning combined with crowd sourced annotations to classify datasets. Smart domain feature allows users to manually annotate columns with domain labels and then machine learning techniques are used to propagate these labels to other “similar” columns. Additionally, users can create custom attributes to classify and facet data assets across multiple dimensions. Finally, self-service BI users can use these classifications while searching for data assets and understanding all the context around the data asset before they use them in their analysis.
For BI users, Enterprise Information Catalog supports extracting reporting metadata from multiple BI platforms including: Tableau, Microstrategy, IBM Cognos, SAP Business Objects, OBIEE and more. While there is no denying that self-service is the way ahead, it needs to be balanced with the right amount of governance “buck” for the big analytics “bang”. Enterprise Information Catalog with automatic data assets scan, powerful search indexes and a machine learning based classification platform is what the Self Service BI users need from becoming data heroes to superheroes.
Source: Informatica Perspectives
Source: Informatica Press Releases
Source: Informatica Press Releases
Recently I was speaking to a customer in the financial services industry. He had an interesting requirement. He wanted toRead more…
Recently I was speaking to a customer in the financial services industry. He had an interesting requirement. He wanted to classify all data assets in the organization by associating the right semantic label with each data asset. The phrase he used was “classify to completion”. According to him this classification was a key first step in cataloging, governing and extracting value from data assets in their organization. We agreed. Finally someone that understood the importance of data domain discovery!
Informatica Enterprise Information Catalog has the capability of identifying semantic label of a column by evaluating data patterns and metadata. These semantic labels are called Data Domains. Data Domains are rule based where rules can be defined as regular expressions, reference tables and more complicated expressions provided through Informatica’s mapping language.
An example of a rule based data domain is Email. A simplistic regular expression to identify email is:
To create a data domain for Email then you would create a new mapplet that returns TRUE every time this regular expression matches. Data Domain Discovery looks for matches against each value in a column and associates the data domain if it finds a large proportion of the values matching the pattern.
Enterprise Information Catalog ships with 60+ out of the box data domains and users can create their own to automatically classify data assets across the enterprise.
Problems with Rule based Data Domains
However, there are two key problems with Rule based data domains:
- Scale: Consider a large enterprise. It is common to find thousands of databases with columns numbering in 100s of millions. To classify all these columns to completion it is estimated that users may have to create around 20000 data domains. The organization will need an army of people to create individual mapplets and rules for 20000 domains. Certainly a process that cannot scale.
- False Positives and Negatives: Consider a data domain like “Age”. It matches against any number between 1 and 120. This broad definition results in a high number of false positives. Instead a smarter system would consider other metadata like column name, other columns in the table(Age occurs with First Name, Last Name, Email ID etc.), distribution(age distribution for customers will be very different for a financial services firm vs. a business targeting college students ) etc. to score the match.
Enter Smart Domains
This is where Smart Domains fare much better. Smart Domains do not require pre-created rules. Instead Smart Domains learn by example associations. Users can directly associate a smart domain with a column after which the system learns from the association and auto-propagates this domain to existing and new columns similar to this one.
Kind of like how Facebook suggests tags when someone uploads your photo.
Facebook compares the new photo against existing photos that have already been tagged to provide these suggestions.
Smart Domains are the equivalent of facial recognition for data sets.
Data Similarity is one of the key factors used for suggesting data domains*. Data Similarity computes the extent to which data in two columns are the same. However it will be computationally prohibitive to try and compare all two-column pairs in an enterprise setting.
As an example, with 100M columns there are 5000 Trillion column-pairs to compare.If evaluating each pair took a nanosecond, the calculation would take roughly 5 Million seconds which is about 58 days!
Instead Data Similarity uses machine learning techniques to cluster similar columns and identify the most likely matches. This process uses the underlying big metadata platform for the clustering job.
Once the similar columns are identified, they can be used for multiple applications including Smart Domains. Others are:
- Recommending related data assets: An analyst working at a telecom company might be interested in doing customer churn analysis. She might start by querying for assets containing customer activity and find a spreadsheet containing call records of customers for the current quarter. Using Data Similarity, the system can recommend:
- A cleaned up version of the same data (substitutable data)
- Another table containing call records for previous quarter (union-able data)
- A customer detail table that might be joined to enrich the data with customer information (join-able data).
- Identifying Duplicates: An organization can save storage costs by getting rid of unused duplicate data assets. Data Similarity will help to find these duplicates across the enterprise
- Inferring Data Lineage: Lineage metadata today is extracted from sources like PowerCenter, Big Data Management, Informatica Cloud etc. However in many cases customers use hand-coding or processes like FTP to move data around. Using data similarity the system will be able to infer such lineage relationships automatically.
Column Level Data Similarity is available with Enterprise Information Catalog v10.1.1.
*While Data Similarity is the first factor we are using for propagating Smart Domains, we plan to expand that list in the future to include additional factors like Data Patterns, Column Metadata, Lineage etc.
The post Classify to Completion: A Data Cataloging Story appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.
Source: Informatica Perspectives
Digital Transformation has become a major agenda item for many Insurance institutions. In a recent blog, I looked at DigitalRead more…
Digital Transformation has become a major agenda item for many Insurance institutions. In a recent blog, I looked at Digital Transformation for Retail Banking (link here).
In this blog, I wanted to explore what I see happening in the world of Insurance and the role that data plays in this industry. My view has been that Digital Transformation is similar, but not the same, in Retail Banking and in Insurance. Here I want to share what I see happening in Insurance.
Insurers are changing
Like the Retail Banking sector, Insurance institutions are considering what their value proposition needs to be and how to respond to market changes. Apart from mandatory Insurance products, such as vehicle insurance, the younger generations are increasingly asking whether they need many other insurance products and are willing to shop around to get the right deal. There are other, broader reasons for these changes that include:
- The need to understand who the customer is and what they’re expectations and needs are. With more objects that need insuring plus a larger, older generation needing tailored products; the need to know the customer is getting more important.
- The Millennial generation have higher expectations of providers including omni-channel access, real-time processing and mobile everything; whereas Insurance very often works at a much different pace and in a different way.
- With so many commoditised Insurance needs, players from other industries are entering traditional Insurance institution markets and bring a different experience.
- The need to compete better, drive operational efficiencies and connect more with the objects they Insure means Insurers are looking at ways of reducing costs and improving service by using new technologies including Blockchain, artificial intelligence, machine learning and the Internet of Things.
- Regulation and compliance is still a major issue so how do Insurance institutions deliver these requirements both effectively and efficiently. Some obvious candidates could include SOLVENCY II and the new EU General Data Privacy Regulation (GDPR).
- Insurance can often be considered quite transparent, especially for low touchpoint products. So how do Insurers create more product and brand visibility to a generation of customers who are used to being marketed to?
Like Retail Banks in my previous blog, I think the question many Insurance institutions are asking is ‘How do we build a value-add relationship with Customers, to secure their business today and tomorrow?’ and move from just being a ‘service provider’ to more of a ‘relationship partner’. Given the notion of what Insurance fundamentally is, the move to being a ‘relationship partner’ can therefore be quite challenging.
So I think Insurance institutions are seeing Digital Transformation as driving change in their business and engagement models; sometimes at a very fundamental level. These changes could include:
- Putting the ‘Customer’ at the heart of the business i.e. really being Customer Centric.
- Moving added value interactions to digital channels.
- Digitalisation of the Value Chain.
- Getting closer and connected to the object(s) they insure.
So why are Insurers investing so much in Digital Transformation?
I think this is because many Insurers can’t innovate fast enough to keep themselves relevant and nimbler players are eroding market share and margins. Also, I think another challenge is that often there isn’t a single, consistent definition of what Digital Transformation is for an Insurance institution. Here’s my definition, based upon the many Insurance institutions I’ve been to visit:
Digital Transformation is the application of digital technologies and solutions, to drive the adoption of disruption and innovation as the basis of change.
The last part of this definition, about disruption and innovation, is something most Insurance institutions are very aware of. The challenge is how to respond to it.
So why is Digital Transformation so challenging?
I think the world of Insurance still has many data challenges, which makes Digital Transformation difficult. Some common data challenges I’ve observed include:
· Data in Silo’s
· Cloud / On-premise data stores
· Inconsistent data definitions
· Unclear data ownership
· Complex change management
· Complex organisational structures
· Incomplete Data Governance
· Data latency challenges (e.g. IoT)
· Legacy systems
· Lack of data standards
· Data complexity
· Data Culture issues
· Complex business models
· Data security requirements
· Lack of data sharing
· New technology introduction
· Lack of data value understanding
· Business / IT integration
· Mixed quality data
I could go on. But the point is that even with these challenges, Insurance institutions needs to transform their business models otherwise they risk being unable to compete.
Digital Transformation capabilities for insurance
To help demonstrate this I’ve mapped out some of the capabilities I think an Insurance institution might need as part of a Digital Transformation programme, to try and explain the importance of the role of data to that programme. A sample of the capabilities are:
I’ve highlighted 5 capabilities as good examples of some Insurance specific capabilities where data plays a very important role.
- IoT data services: data can have very different latencies and can also include semi and unstructured content; which means some Insurers are struggling to gain value from this capability as they’re not equipped for it.
- Broker services: intermediaries often hold the customer relationship and history but, with more connected objects sending data directly to an Insurer, the needs of data sharing are changing.
- Claims automation: incorporating digital data into the claims process, and automating as much as possible, means Insurers can’t afford for data to be any form of process bottleneck.
- New data acquisition: for many low touchpoint products, alternative sources of data are required to help build out a more detailed picture of the customer journey yet finding and incorporating these sources can be challenging.
- Customer Centricity: many Insurers are still on the product-to-customer centricity journey so aligning processes, centred around the customer, can still be challenging.
My conclusion was that to deliver these Digital Transformation capabilities, there is a high dependence upon the data each capability generates and/or consumes. I also concluded that a single Digital Transformation capability may be dependent upon many other Digital Transformation capabilities to deliver their expected benefits. This suggests to me there is a significant impact upon the success of any Digital Transformation capability from all the data they require, to operate correctly.
Below is a picture of what data capabilities might be needed to deliver the Digital Transformation capabilities outlined above. It’s a generic set of data management capabilities but hopefully you’ll get the idea of what’s needed:
Where this start to get interesting is when you map out what data capabilities are required for each Digital Transformation capability. What you start to see is how many of the data capabilities are required for each of the Digital Transformation capabilities. You can also see that there is plenty of overlap, and scope for reuse, of the same data capabilities as you look at the bigger picture of what’s required for Digital Transformation. Below is a picture to show this:
From the picture, you can see many of the same data capabilities are required across all three Digital Transformation capabilities used in the example. This picture gets even more compelling when you map out all the data capabilities required for the rest of the Digital Transformation capabilities.
So, what does this all mean?
The overlap in data capability requirements leads me to conclude that delivering all these Digital Transformation capabilities, with the right data at the right time, requires a platform approach to managing data.
Informatica developed the Intelligent Data Platform (IDP) to provide the data platform capability that could support many different types of Digital Transformation programme.
For those of you that read the Retail Banking version of this blog, you’ll notice some similarities and some differences. That’s to help highlight the similarities and differences in what Digital Transformation means between Retail Banking and Insurance. The point is that Digital Transformation isn’t the same across every industry and that, even between two similar industries, the differences can be quite significant.
Source: Informatica Perspectives
What to do if business stakeholders derail your plans The biggest challenge of making sure your cloud transition fits intoRead more…
What to do if business stakeholders derail your plans
The biggest challenge of making sure your cloud transition fits into your overall IT architecture? Sticking to the plan. You map out a transition journey that includes new systems, training and orientation, retirement of old systems, maybe some new hires, probably some new vendor relationships. But then someone in the business comes to you with an immediate need that would contradict, delay or derail your careful planning. Now what?
The journey to a cloud-first or hybrid IT infrastructure is a progression of hard choices. I can remember vividly conversations with business people that boiled down to, “How far can we go in building suboptimal solutions or infrastructure to meet immediate needs, without totally scrapping the IT roadmap?” Yet I’ve never seen my customers look back at that short-term fix and say, “Great call—glad we did that.” More often it’s, “Why couldn’t we have held off for six months, even three, for a better, longer-term solution?”
But IT has spent a decade struggling to “align with the business,” so it’s hard to say “no” to a business need. But there is a way to make those tough calls, and stand your ground when necessary—intelligently, effectively and diplomatically. Here are four tactics to use with your business stakeholders.
- Be creative
The requests that disrupt a cloud journey most often involve a new investment in a legacy system. If you really understand the business need in question, you may be able to find more options than a straight yes or no. Today’s IT world offers far more ways to quickly spin up an environment for just one subset of users, or to give them an alternative set of data, or something outside the application that they’re using.
In addition to understanding the tech options, you need to understand your end-user. To satisfy a business need—not merely the technology request—IT needs to understand the problem. Get the actual user and the actual solution developer talking. After all, the only way to be creative is if you really understand what you’re solving for.
- Head them off at the pass
You can’t always pull a creative solution out of your hat, though. And sometimes you’ll need to stand firm for the longer-term IT vision. It’s important to establish strong executive agreement with an IT strategy that is, of course, closely tied to the company’s vision. When a conflict over a business request escalates from midlevel business managers to their bosses, the answer they hear should be, “Don’t you know that we have a strategy here?”
- Show them the money
The ultimate arbiter when short-term gain conflicts with long-term plans is money. Defend your IT roadmap in the language of business. It should include some baked-in numbers quantifying how your IT evolution will contribute to the enterprise’s bottom line. Explain the price tag of any proposed deviation, in terms of higher costs, lost/delayed revenue, etc.
This discussion puts everyone on the same page. It’s not about a CIO’s attachment to a carefully crafted vision of the perfect IT environment. It’s not about a line-of-business manager’s desire to hit his or her quarterly numbers. It’s about the overall good of the company. And that means sometimes IT needs to look at the numbers and say, “Yeah, this request does slow us down by six months, but it’s so important, it’s worthwhile.” Then look for ways to minimize the downside.
- Know when to let go
Letting financial considerations rule can be difficult for tech-focused people who take pride in their work. No one wants to implement a suboptimal solution or sacrifice a singularly elegant IT vision. But IT leaders have to learn to be comfortable with a quantitative discussion rather than a qualitative one. It can take some time and effort to adopt that business perspective, but it’s a must.
The cloud journey is not quick or easy. Keeping it on track is one of the biggest difficulties. Sometimes you have to dig in your heels, and sometimes you have to find the smartest compromise. Understanding the bottom line, fostering high-level buy-in, and approaching challenges creatively are the best ways to keep your transition moving forward.
My next post will tackle a specific way to get cloud rollouts right, by looking at the key role of data governance in a cloud or hybrid enterprise. In the meantime, if you have other tactics you use with business stakeholders so your initiatives don’t get derailed, please share in the comments below.
Complimentary workshop with Informatica Implementation Architects
To help kickstart your cloud journey, or to get it back on track, we’re offering a complimentary 90-minute workshop with an Informatica Implementation Architect. During the session, we’ll assess your current state, where you want to be on your cloud journey, and the steps you need to take to get there. After the session, you’ll get a cloud reference architecture and customized recommendations for technologies and services, as well as immediate next steps. Here are a couple of slides about the workshop. To find out more and to register, please contact your Informatica Account Rep, or email us.
Source: Informatica Perspectives
Source: Informatica Press Releases
Source: Informatica Press Releases
Marketing Analytics for CMOs If you haven’t seen it yet, I want to recommend the Informatica Naked Marketing blog seriesRead more…
If you haven’t seen it yet, I want to recommend the Informatica Naked Marketing blog series written by my marketing colleagues. In it, we give full details of how we built a B2B marketing data lake from the ground up. The data lake provides all our marketers, field marketers, and sales with valuable, connected data about our customers and prospects gathered from all their interactions with our website, digital content, digital advertising, and social media. The journey to the data lake wasn’t easy; the biggest challenge was that our data was siloed in various apps, which prevented us from connecting a true end-to-end marketing journey.
Today, we have an exciting update on how we can help you address this data silo challenge for your marketing organization.
Informatica works with Google to serve marketers
Informatica is working with Google to help marketing professionals build a next-generation data-driven market engine in their organizations, with new capabilities in marketing analytics and data warehouse modernization. With Informatica, combined with Google Cloud Platform, customers can easily pull in marketing data from multiple CRM, marketing automation, and social systems such as Salesforce, Marketo, and Eloqua to augment their data from existing Google marketing services, and enable powerful end-to-end customer insights.
With Informatica’s out-of-the-box cloud connectivity for Google Cloud Platform’s, data analytics services such as Google Big Query, and recently announced Google Spanner (see our press release), customers can modernize their marketing data warehouse implementations and easily move to a hybrid or cloud-native marketing analytics environment.
The result enables CMOs and their team to address many of the biggest data and operational challenges in moving to a high performing data-driven marketing organization.
Becoming a data-driven marketer
My colleague, Anish Jariwala, Informatica’s director of digital marketing strategy and analytics, led the creation of our marketing data lake and has worked with marketing data all of his career. He has some great insights to share about why it’s important for marketing leaders to be data-driven, the challenges they face creating a data-driven culture and how best to do it. Below is what Anish shared with me in his own words:
CMO or (C-1) marketing leaders have to recognize the fact that over the last 10 to 15 years, marketing departments have turned upside down because of the amount of data their department collects in their individual marketing apps, such as web analytics, marketing automation, CRM and social. Also, this data is siloed, preventing marketers from mapping end-to-end customer journey that can create a true marketing impact.
Increasingly, CEOs and CFOs are asking CMOs for the spend accountability and show ROI on marketing spend. The simple marketing mantra is: do NOT spend money if you cannot measure it. Gone are the days of spending millions of dollars on marketing initiatives that aren’t targeted and relying on a “spray and pray” approach.
I still recall one CMO interaction that I had when I worked at Marketo. The CMO mentioned that she had to show value to sales in eight weeks using the Marketo product. I bet this kind of communication has become common in the modern marketing apps-driven world. Every time, we fire up our Account Based Marketing dashboard in front of customers and prospects, the overwhelming reaction is: “Can we ever get there?” and “How long would it take?”
Marketing leaders have to foster a data-driven culture and promote data-driven unified insights for each and every marketing decision in their meetings. This forces marketers to find data to test their hypothesis. Also, the by-product of this approach is that marketers develop data analytical skills that increase their market value.
Execution has become a critical glue in digital marketing initiatives. If execution is an issue, you spend your precious resources—money and time—with delayed or no outcomes in sight. If you fail, you need to fail fast and adjust accordingly. Many marketing teams have a great vision but they fail in the execution phase.
Source: Informatica Perspectives