Translating data Into progress for 2017


Imagine having complete visibility across your business to quantify exactly how and where resources are being used, so you are always ready to seize new opportunities.

In an age of data, this is not wishful thinking. We already have all the information. We just need to understand it.

Translating Raw Data

The raw data exists, but most businesses have trouble interpreting it. They are unable to put their data into a context that can provide real businesses insights and objective outcomes, which limits decision-makers from making important business decisions.

There must be a cultural shift towards the importance of reading data and utilising the information at our fingertips for the best outcomes of the company. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.

But how will businesses make culture-wide data literacy become a reality? Here are my predictions for 2017 and beyond:

1. Data combinations 

Data size will matter less in comparison to the insights gained from its collation. With more fragmentation of data and most of it created externally in the cloud, there will be a cost impact to hoarding data without a clear purpose. Businesses will have to quickly combine their big data with small data so they can gain insights and context to get value from it as quickly as possible. Combining data will reveal discrepencies, improve data accuracy as well as its understanding.

2. Hybrid thinking

In 2017, hybrid cloud and multi-platform will emerge as the primary model for data analytics. Data has been shifting to the cloud – because of where data is generated, ease of getting started, and its ability to scale. One cloud is not enough, as the data and workloads won’t be in one platform. However, in an age of data protection, on-premise infrastructure will have long staying power, as organisations look to safeguard data. As a result, hybrid and multi-environment will emerge as the dominant model, meaning workloads and publishing will happen across cloud and on-premise.

3. Self-service for all

Freemium has become the new standard, so 2017 will be the year users have easier access to their analytics. More and more data visualisation tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce. As analytics participation rates increase, so will data literacy rates naturally increase — more people will know what they’re looking at and what it means for their organisation. That means information activism will rise too.

4. Scale-up

User-driven data discovery has evolved into today’s enterprise-wide Business Intelligence (BI). In 2017, this will evolve to replace archaic reporting-first platforms. As modern BI becomes the new reference architecture, it will open more self-service data analysis to more people. It also puts different requirements on the back end for scale, performance, governance, and security.

5. Advancing analytics

In 2017, the focus will shift from “advanced analytics” to “advancing analytics.” Advanced analytics is critical, but the creation of the models, as well as the governance and curation of them, is dependent on highly-skilled experts. In addition, analytics can be advanced by increased intelligence being embedded into software, removing complexity and chaperoning insights. But the analytical journey shouldn’t be a black box or too prescriptive. Additionally, artificial intelligence will continue to play a vital role as an augmentation rather than replacement of human analysis because the right questions are just as important as the answers.

6. Visualisation on the whole information supply chain

Visualisation will become a strong component in unified hubs that take a visual approach to information asset management, as well as visual self-service data preparation, underpinning the actual visual analysis. Furthermore, progress will be made in having visualisation as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.

7. Customising your own analytics apps

Everyone won’t — and cannot be —both a producer and a consumer of apps. But they should be able to explore their own data. Data literacy will therefore benefit from analytics meeting people where they are, with applications developed to support them in their own context and situation, as well as the analytics tools we use when setting out to do some data analysis. As such, open, extensible tools that can be easily customised and contextualised by application and web developers will make further headway.

These trends lay the foundation for increased levels of not just information activism, but also data literacy. After all, new platforms and technologies that will be more accessible and normalised in work processes will help usher us into an era where the right data becomes connected with people and their ideas — that will close the chasm between the levels of data we have available and our ability to garner insights from it. Which, let’s face it, is what we need to put us on the path toward a more enlightened, information-driven, and fact-based era.

About the author

Sharryn Napier is the Vice President & Regional Director, Australia & NZ, with business intelligence software company Qlik.