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TDWI Upside - Where Data Means Business

CEO Perspective: Staying Ahead of Data Management Challenges

TimeXtender's founder and CEO, Heine Krog Iversen, explains today's best practices for tackling data management issues.

What do data access demands, lift-and-shift to the cloud, AI, and the futility of adding more tools have in common? They're all part of the challenges of data management that enterprises must deal with now and for the future. TimeXtender's founder and CEO explains what's ahead in data management and the best practices enterprises can employ to deal with the challenges.

For Further Reading:

Building Your Data Management Strategy

The New Ethics of Data Management

Big Data Management Comes of Age

Upside: What technology or methodology must be part of an enterprise's data strategy if it wants to be competitive today? Why?

Heine Krog Iversen: The right strategy needs to be in place to cover any existing and future use cases of data. It is increasingly difficult to predict what will be needed in the future, but one thing is for sure: demand for data will continue to grow. Enterprises need to have a methodology where all relevant data is offered to the business just as it is -- as data -- and not modelled out to support specific, existing reporting needs.

Donald Farmer states that IT needs to be data shopkeepers and not data gatekeepers and that will become even more true in the future. We need to recognize the need for data compliance and security rules but at the same time realize that the solution is notto lock down access to data. We need to be brave in sharing data and have a data platform in place where we can track data use, examine data lineage, and document what is going on with our data while it is being used by the entire organization.

In the future, we cannot accept delays to data access in any form and we cannot require repeatedly doing the same work to provide data to the business. The days with a backlog for IT to provide access to data for analytics and AI are over. To eliminate delays, we need to make sure that the business does not need to redo their data prep work every time they have a new task or data point.

To be able to support all of the above, the strategy should be based on the cloud, and the data architecture should consist of data lakes, data warehouses, and semantic models and data hubs to provide data for analysis by all types of users.

What one emerging technology are you most excited about and think has the greatest potential? What's so special about this technology?

AI in general, but more important, automated AI is looking very promising. If we really want to have the full benefits of AI, but also make sure we are using it in an ethical manner, the more automation we can add to the use of AI the better. The world is lacking not only data scientists but the ability to bring AI to more business people with fewer skills in the area. However, the potential to help them achieve more is promising.

In addition, democratizing access to AI and utilizing new algorithms to help more people gain insight into their business is very interesting. Eventually, with the right data estate and data infrastructure in place, users can automatically get insight and predictions that they would never have the skills or the resources to produce otherwise. This same technology will also make AI available to small companies that will likely never have the resources to employ a team of data scientists.

If a company makes sure it has the right data foundation in place, it can be a real game changer for its business.

What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?

For Further Reading:

Building Your Data Management Strategy

The New Ethics of Data Management

Big Data Management Comes of Age

When the data estate is fragmented and built in silos, it prevents enterprises from utilizing new opportunities as they emerge. Enterprise customers have it all: data lakes, data warehouses, and lots of front-end tools. All of these are built as silos, with multiple tools and a lack of documentation. Often, this is deployed on premises, with no clear strategy about the business user or about what the future will bring. The typical solution is to add more tools (such as data prep tools) to the business and add new teams to handle the workloads.

Also, I see that a mere lift-and-shift to the cloud is seen as a solution, mostly focusing on cost and performance and less on the value of the data. Very often, it turns out that moving to the cloud or adding more tools is counterproductive because the landscape is getting more complicated.

What is needed (together with the move to the cloud) is to rethink the entire approach and organizational setup to prepare for the future.

Is there a new technology in data and analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?

In general, the concept of the data-driven organization creates lots of challenges. The more technology we give to the business, the more demand we see for data and data we can trust. It turns out that most enterprises are still spending 80 percent of their time getting to the right data rather than on using the data. This lowers the value of data initiatives throughout the organization.

Data needs to be in a better shape, instantly available to the business in the formats users need to help gain greater and faster value from the use cases.

What initiative is your organization spending the most time and resources on today?

TimeXtender is finalizing its own internal data estate on Azure, adding all relevant data sources to give the entire organization instant access to all data. This is key to making any enterprise truly data-driven and essential to staying ahead of the game and serving our partners and customers in the most efficient way.

We also spend significant amount of time on enriching the data we capture in our source systems and using automation to have it added to the data estate at the speed that we collect new types of data.

Where do you see analytics and data management headed in 2019 and beyond? What's just over the horizon that we haven't heard much about yet?

Enterprises will realize that adding more tools is not solving their problems. We are already seeing a consolidation on the vendor side and a battle between cloud providers to get the workload.

With that said, it's all still based on the same strategy we have seen during the last 30 years when it comes to implementation. More specifically, this means consolidating tools into a complete data management platform based on automation and AI, but also mixed with a modern approach that addresses all the benefits of building data estates. Taking this approach will change an organization to one based on speed and agility. At the same time, organizations need to incorporate AI/ML technologies that exist today and that will become prevalent in the future because these types of offerings continue to rapidly offer new capabilities to improve data management and analytics processes.

One final consideration is the fact that purchasing more tools and executing a lift-and-shift to the cloud based on cost calculations is not really delivering value when it comes to utilizing the data. There isn't anything wrong with saving money, of course, but if we are not also enabling the business to move to the next step, we risk wasting all those saved dollars by being left behind.

Describe your product/solution and the problem it solves for enterprises.

Discovery Hub, from TimeXtender, helps companies rapidly create a modern data estate by moving from a patchwork of data management tools to an integrated platform that accelerates time to data insights by up to 10 times. Providing automation and capabilities for data access, modeling, and compliance, Discovery Hub provides a cohesive data fabric across Microsoft on-premises technology and Azure Data Services, allowing you to connect to various data silos, catalog, model, move, and document data for analytics and AI.

[Editor's note: Heine Krog Iversen is the founder and CEO of TimeXtender, a global software company that provides instant access to data for analytics and artificial intelligence (AI). It serves more than 3,000 customers, from midsized companies to Fortune 500 enterprises, through its global network of partners. TimeXtender was founded in 2006 and is privately owned, with headquarters in Denmark and the U.S., and regional offices around the world.]

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