A few years ago, only big corporations with a focused objective of rapid growth invested in augmenting themselves from the inside out to thrive in a potentially highly competitive future. But today, even SMBs are coming forward to invest in business transformation approaches that involve capitalizing on powerful new digital technologies and trends.

Modern businesses need to operate more efficiently and establish themselves in new markets faster than before. Additionally, many businesses are also focusing on improving customer experiences in order to improve business outcomes. It’s safe to say that business transformations will be gaining even more momentum this year.

However, achieving such a transformation is a big challenge in itself. As a first step, IT teams should devise a digital strategy that addresses all the limitations of the business including legacy technology limitations, and make the most of all the strengths of the business.

Here are a few important trends that could help a business succeed in transforming itself efficiently.

Focusing on one digital approach

A recent DXC Technology report found that companies tend to invest in a single digital approach to accelerate business transformation. There certainly will be a lot of big strategic commitments involved. Another surprise is the fact that many organizations are actually avoiding hybrid traditional digital strategies and going for an approach that can essentially unify their business without compromising its integrity and security.

Leveraging next-generation IoT platforms

As new IoT platforms pop up, businesses are exploring new opportunities to merge their traditional data sources to new ones, obtain more accurate data inputs, enhance existing real-time data analytics capabilities etc.

Cloud & AI for a smarter IT infrastructure

Businesses are closing in on realizing the idea of a smarter IT infrastructure. To make it happen, many businesses rely primarily on the cloud and Artificial Intelligence. An effective combination of the cloud and AI can help build an ecosystem driven by AI-powered applications with nigh unlimited resources. Such applications grant more power to users while also offering lower reaction time and better, more localized analytics.

Improving services with better decision-making capabilities

Companies have realized that data is the key to a faster growth. They now mine tremendous amount of data to uncover valuable information that will help them make informed decisions. This trend has led to an increasing demand for AI & ML-based tools and big data analytics capabilities. Making more educated decisions subsequently improve the services an organization offers.

Shutting down enterprise data centers

Data centers are slowly going obsolete, and it is evident this year. The proliferation of the cloud led companies to shift their workloads onto the cloud. Many companies move their mainframe workloads to specialized data centers while most others are shutting down their data centers. Traditional data centers are running out of steam. But the bright side is that companies can serve distributed customers better and access higher bandwidths to improve functionalities.

Conclusion

Business transformation is the initiative enterprises are most keen on taking this year. They adopt different approaches for the purpose but often fail to realize that sometimes it’s the little things that could have the biggest impact. The trends mentioned in this blog should help an enterprise in its venture to trigger a transformation to thrive in a competitive world. If your enterprise is on the lookout for next-generation digital solutions that can facilitate digital transformation, AOT can help you build one instead. Talk to our experts to learn more.

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Many factors influence business transformation and evolution today. But among all those factors, data stands out unrivaled. The data that a business generates hide the key to the business’ evolution with respect to its operations. However, it’s not easy to mine those valuable information from such tremendous amounts of data unless big data analytics is involved. Modern day technologies make it possible for businesses to uncover valuable insights from the data and figure out the key to dominate in their respective industries.

So basically, a business needs advanced data management and analytics capabilities, data scientists, and an efficient infrastructure to leverage their own data to trigger an evolution that keeps up under dynamic market conditions and enables them to stay ahead of the competition.

Realizing this, many businesses today try to utilize the power of big data analytics to improve themselves. But not all of them succeed. Successful big data integration is a challenge in itself, and that’s what we are going to discuss about in this blog.

Bringing big data into the picture

Data integration impacts the processing, collection, and transfer of data within an organization regardless of whether the data is old or new. Big data integration is for large, complex data sets. The integration itself is complex, and should be done carefully while keeping in mind the importance of the resulting accuracy for better decision-making.

Big data analytics is a whole another process with which businesses can reap great benefits. We’ve discussed that bit before. But integrating big data is the first challenge to be addressed.

Here are a few things to consider for successful big data integration.

The right talent

Big data combined with disruptive technologies like AI and Machine Learning can deliver astonishing results. In addition, many new tools evolved in the big data sector recently – from modern relational database tools to data layouts designed to reduce storage footprints and improve accessibility, which can make things much simpler for enterprises.

The new Hadoop ecosystem, the advancements in analytics, efficient data management frameworks etc. present enterprises with different types of approaches for integrating big data. However, it’s easier said than done because to do all this, the enterprise will need the right talents. It’s best to rely on people or organizations with great experience when it comes to leveraging big data technologies.

Data quality is a priority

Many businesses tend to give data quality a thought after implementing big data analytics. This is certainly not a wise approach. Data quality should be prioritized before big data integration. There will be semi-structured and unstructured data involved which can complicate data set integration.

The analysis itself requires relevant, good quality data to deliver good results. The business needs to assess their data quality just to check whether they can have relevant data sets to feed to the algorithm once the integration is done. Just a lot of data is not the only condition that should be satisfied for implementing big data.

Data is your asset

The seemingly trivial data that a business generates, if mined the right way, hides a lot of real and assessable values. Not all data are valuable however. Data science helps differentiate relevant data. The point is that a business’ data is an important resource that helps make better decisions in the future leading to better productivity and enhanced growth rate. While integrating big data, make sure all the relevant data are safely secured, managed, and accessible. Data is an asset and should be treated that way rather than as a means to better times.

Be aware of the risks involved

Big data and analytics are sophisticated. The complexities obviously emphasizes the presence of risk factors. With big data analytics, the business gets to visualize and predict several outcomes. But the accuracy of these predictions depend on how well the integration was done. Doing it the wrong way may lead to unfair results, erroneous analyses, and misleading numbers.

This is why it’s crucial, during integration, to understand how things can go wrong, where things can go wrong, and how wrong things can get. Risk assessment helps in integrating big data the way it should be integrated.

Possible synchronization issues

Once the big data platform is online, data will have to be imported from multiple sources for analysis at different rates and on different schedules. This means there is a good chance for the data to go out of sync with the originating system. For instance, the data from one source might be outdated while data from a different source isn’t.

In addition traditional data management, data transformation sequences etc. also raises the risk for the data to go out of synchronization. This should be taken into account before big data integration, so as to devise a data migration management strategy that minimizes risks.

Various challenges

It’s important to be ready for any challenges that arise while integrating big data, including big data solution cost and compatibility, data volume, data transformation rates, data validation mechanisms, associated expenses etc. Another challenge is to implement a system where the data are processed at high speeds so the results are delivered and made accessible on time. Achieving that level of performance will require bespoke solutions and qualified technical support.

As of now, considering where technologies stand, an enterprise is better off seeking assistance from an experienced big data specialist and data scientists rather than investing in an in-house team. Laying a solid foundation to big data is the first step to using big data the right way. That’s why the initial steps should be taken under the supervision of big data experts.

AoT’s expertise in big data comes from our experience and commitment when it comes to mastering disruptive technologies so we can leverage them the right way. We can help integrate big data into your business while reducing associated risks. If you like to learn more about big data integration and how we can help with it, get in touch with us.