Some expert advice on gaining organizational trust

Take a moment and ask yourself, what is your definition of trust and how do you know when you are trusted?

Did the answers come quickly, or not? If you don’t have a ready definition, don’t worry, most people don’t. It’s just one of those things that we have an impression about. We know it when we feel it. Here’s the bad news. It’s hard to know when you are trusted and even harder to know how to build it. And on average, we need to earn a lot more trust than what we currently have. The good news? It’s possible to earn trust with the right plan.

What’s that you ask? Doesn’t ‘planning for trust’ sound Machiavellian? I can imagine that it must, but here’s more good news. The plan not only can help you build trust, but it can also help you make a few friends along the way. The same tactics you use to build trust, are some of the same you might use to build relationships and gain friends. The problem is that we leave too much to chance and we don’t always know what works. Don’t do that. Instead, do this:

1. Understand the context in which you want to earn trust. If you are a CIO working on getting a seat at the table or build out a digital transformation plan, that context is far different than the context of traditional IT and how you may have built your brand so far.
2. Recognize that trust is developed based on your expertise and knowledge within that context.
3. Acknowledge that trust is also developed based on the rapport and relationships you build accordingly.

Trust is contextual

The determination of trustworthiness happens within a particular context. A pediatrician is someone trusted to treat a sick child, but not to manage a problematic investment portfolio. A CIO and their team may have built trust in the context of building and running robust and predictable back-office IT systems, but they may be untrusted to create innovative and flexible solutions for end consumers. Paradoxically, past success is not an indication of future success. Studies have shown that people who have been successful in the past are actually more likely to perform worse in a new context. They can become overconfident and less open to feedback.

Trust is a measure of expertise and rapport

CIOs may try to develop trust by first demonstrating expertise in all things digital. However, unlike consultants or contractors, who are external parties and can rely on expertise alone (part of an open social system), the CIO must rely on expertise plus rapport (part of a closed social system), but rapport leads the way. Focusing on expertise can diminish perceptions of warmth. Instead of coming across as an expert, CIOs risk coming across as overconfident or arrogant. Building rapport requires empathy, listening, curiosity, and genuine interest. Compare that to how one demonstrates expertise using logic, opinions, and arguments. It is wiser to have built some rapport before asserting opinions and arguments.

Author: Ed Gabrys

Source: Gartner

Why trusting your data is key in optimizing analytics

With the emergence of self-service business intelligence tools and platforms, data analysts and business users are now empowered to unearth timely data insights on their own and make impactful decisions without having to wait for assistance from IT. It's the perfect situation for more agile, insightful business intelligence and therefore greater business advantage, right?

The reality is that even with these new BI tools at their fingertips, most enterprises still fall short of leveraging the real power of their data. If users don't fully trust the information (even if they're able to find and comprehend it), they won't use it when making business decisions. Until organizations approach their data analytics strategy differently - by combining all aspects of how the data is managed, governed, prepared, analyzed, and shared across the enterprise - a lack of trust will prevent a business' data from being useful and leading to successful business decisions, ultimately turning it into a liability rather than an asset.

Finding the balance between agility and trust

Although the self-service features of modern BI platforms offer more freedom and greater analytics power to data analysts and business users, they still require enterprises to manage and maintain data quality over time. Various roadblocks impede data analysts and business users from gaining access to the trusted data they need. Businesses can overcome common and critical challenges using tactics like:

Building agility through proper data preparation

Many times, data prep - the process of gathering, combining, cleaning, structuring, and organizing data - is missing from the analytics equation, especially when data analysts or business users are eager to get results quickly. However, having the data clearly structured with a common vocabulary of business terms (typically held in a business glossary of a data catalog) and data definitions ensures that people can understand the meaning of available data, instilling trust.

Because data is pulled from both internal systems and external sources for reporting, profiling and cleansing data is essential to sevure trust in data as well as to improve the accuracy and reliability of results. Any changes made to the data should be tracked and displayed, providing users with the full history of the data should they have questions when using the data.

Freeing (and maximizing) the siloed data

Data is often siloed within different business units, enterprise applications, spreadsheets, data lakes etc., making it difficult to scale and collaborate with others. The rise of self-service BI has made this problem even more relevant as more business users and teams have generated department-specific reports. People working in one silo are likely unaware of what data has already been prepared and shared in other silos, so time is wasted by reinventing data prep efforts and analytics rather than reusing and sharing them.

Integrating data prep with self-service analytics unifies teams across the enterprise - including shrinking gaps between data analysts and the people who have more context about the data - and empowers data scientists with trusted, curated data so they can focus less on hindsight and more on foresight.

Establishing 'a true north' through data governance

Strong data governance practices provide an organization with structure and security for its business data. This is especially critical when data is distributed through many systems, data lakes, and data marts. Governance is the umbrella term for all the processes and rules for data, including assigned owners and data lineage - so users can clearly understand the data's past use, who has accessed it, and what changes were made (if any).

Maintaining balance

For an organization to fully realize the value of its data, it needs a shared, user-friendly approach where all users within a business have easy access to data they can trust to do their jobs, but in a way that is controlled and compliant, protecting data integrity. Organizations can balance the demands for convenience and collaboration with those of control by establishing and maintaining a three-tiered approach. The three tiers in this approach are:

1: The data marketplace

Enterprisewide data use begins with the data marketplace, where business users can easily find (or shop for) the trusted business data they need to gain analytics insights for critical decisions. The data marketplace is where all the rules of governance, shared common data prep, and shared data silos come together.

This data marketplace concept is not a single tool, platform, or device. No single self-service data analytics tool can deliver the results organizations are looking for. The data marketplace is an overarching strategy that addresses data management and discovery with prep and governance to collect trusted data. The marketplace helps organizations address the challenges of finding, sharing, transmitting, analyzing, and curating data to streamline analytics, encourage collaboration and socialization, and deliver results. Creating a standard, collaborative approach to producing trusted, reusable, and business-ready data assets helps organizations establish a common portal of readily consumable data for efficient business analysis.

2: Team-driven analytics

Just as important as having quick and easy access to reliable data is the ability to share data with others in a seamless, consumer-friendly way, similar to how sophisticated online music, movie, and shopping platforms do. Through the data marketplace mentioned above, users can visually see the origin and lineage of data sets just as a consumer can see background information about the musical artist of a song just streamed on Spotify. Through this visualization, users see consistency and relevancy in models across groups and teams, and even ratings on data utilization just as we use Yelp for reviews.

Team commentary and patterns of data use dictate which models are most useful. Similar to sharing and recommending music to a friend, business users can collaborate and share data sets with other users based on previous insights they've uncovered. This team-driven and "consumerized" approach to data discovery and analytics produces quick and reliable business results.

3: Augmented analytics

A newer, more advanced feature of self-service analytics starting to emerge is augmented data insights: results based on machine learning and artificial intelligence algorithms. Using the Spotify example again, when augmented analytics is applied to the marketplace, data recommendations are made based on data sets the user has accessed, just as new music is recommended to consumers based on songs they've listened to earlier.

By automatically generating data results based on previously learned patterns and insights, augmented analytics relieves a company's dependence on data scientists. This can lead to huge cost savings for organizations because data scientists and analysts are expensive to employ and often difficult to find.

By creating this fully integrated approach to how enterprises view and use their data, a natural shift will start to occur for the organization, moving from self-service analytics to shared business intelligence and "socialization", where all users across the organization are encouraged to contribute to and collaborate on business data for greater value and business advantage.

A common marketplace

Organizations that have started to make this shift are already starting to see business benefits. Similar to consumer platforms like Spotify and Amazon, in an interactive community of trust, users thrive and are inspired to share and collaborate with others. I is through this collaboration that users gain instant gratification for more insightful decision-making. Through social features and machine learning, they learn about data sets they otherwise never would have known existed. Because analysts can see business context around technical data assets and build upon others' data set recipes and/or reuse models, they can achieve better, faster decision-making and work more efficiently.

As data complexity increases, the key to realizing the value of business data is pulling all of the different data management and analytics elements together through a common marketplace with a constant supply chain of business-ready data that is easy to find, understand, share, and most off all trust. Only then business data becomes truly intelligent.

Author: Rami Chahine

Source: TDWI

Why we must work together to gain safety and trust in the digital identity age 

As consumers across the globe become increasingly aware of their digital identity and personal data rights and further regulations take hold, it’s unsurprising that Google has announced it will not be replacing third-party cookies with identifiers and email addresses.

Advertisers now need to look for new ways to engage valuable customers on a one-to-one basis. Digital targeting and measurement strategies that the industry has grown up around will need to be rebuilt for a privacy-first world.

This is both a challenge and an opportunity for the industry – to champion privacy while finding new and innovative ways to provide marketers and consumers with relevant, targeted ad experiences. The industry needs to determine the best path forward and partner to develop strategic identity solutions, enabling publishers to maximize the value of their first-party data, help advertisers meet their business goals, and build consumer trust in digital advertising.

A new vision for a new digital identity ecosystem

Collaboration between partners within the digital identity and advertising ecosystem is now more important than ever. Suppose advertisers want to increase the effectiveness of their campaigns across the whole of the Internet. In that case, they need to be working with partners who can join up these conversations without operating a walled garden. Greater collaboration is also vital for local premium publishers to continue developing creative, engaging content for consumers, which is the foundation of their ongoing success.  

The central principle of navigating this changing landscape is for the digital advertising industry to understand where it goes with respect to identity, and it needs to do that with consistency. This means how it will handle identity in the face of the death of third-party cookies, the rise in regulation, and the evolving ways that it is buying and selling advertising today.

Increasing regulation around data privacy – such as the GDPR in Europe – has been one of the biggest drivers for change in our industry. So, advertisers will want to work with companies adhering to data regulations and encouraging transparency within the supply chain. On top of that, many brands will need to feel a sense of ‘safety through familiarity.’ When discussing compliance, it helps to work with a partner with similar challenges, protocols, and internal processes. For example, a bank or a telecommunication company is going to want partners that can demonstrate their security frameworks meets the country’s data privacy standards, as well as your company’s individual privacy standards.  

With cookies, these have been relied on for a very long time, yet we’ve seen over the past year or two that we can generate brilliant performance leveraging solutions that do not rely on this. However, as things stand, there isn’t one silver bullet to identity or one single solution, and it won’t be solved for some time. What needs to be done now is to take a very deliberate multi-pronged approach to solve identity. While first-party data goes some way to achieving this, brands can get market-leading performance and competitive advantage even by just using strong and innovative contextual solutions. It’s important for brands not to stand still at this point; testing innovative new solutions will mean you’re well equipped to deal with what comes next. 

Adopting new models to meet changing needs

For publishers, this means that they need to look at how they can use their proprietary assets to evolve their business models and package and sell their inventory in a way that best meets the needs of the buyer in our rapidly changing digital advertising landscape.

Developing different ways to generate and acquire authenticated first-party data will be one key area of focus for publishers. Many are already doing that as they look to build out subscriber bases. This means that if a person uses their email address every time they visit a site, the publisher can use it as a persistent identifier. From here, they can start to build a profile of that user and what their interests are. By better understanding individual users, publishers’ inventory becomes more valuable to advertising partners, as they can effectively target specific audience profiles. 

Alternative ways that publishers can use their assets, such as building up contextual solutions. The ability to build contextual profiles has advanced greatly since the early days of simply placing adverts for mortgages in financial publications. Today there is much more accurate contextual information about specific articles, so publishers should be looking at utilizing this. Today you can even use contextual solutions to match the sentiment of a piece; for example, if you’re a brand selling retro cameras, you can target context that generates the feeling of nostalgia. 

In the future, publishers will need to consider device-based advertising. If we consider the devices that will support advertising or do already support advertising, very little of that is cookie-based anyway. A raft of different devices will come into play here, such as smart speakers, CTV, and even wearable tech. None of this will be dependent on a cookie, so there needs to be continued investment in exploring these areas and the new audiences they offer. 

With the right data protection, privacy controls in place, and the right partners on board, it remains possible to provide consumers with critical choices and insight into the value exchange of advertising and content. By these means, we can also ensure that we enable publishers and marketers to achieve the required outcomes. At this point in time, the worst thing you can do is stand still and wait for something to happen around you. Your audience is still there online, so it’s important that you take all the steps necessary to continue connecting with them.

Author: Karan Singh

Source: Dataconomy