May 19, 2020

Data: how to manage your company’s most valuable commodity

Data
Big Data
GDPR
Data infrastructure
Pouyan Broukhim
4 min
Data: how to manage your company’s most valuable commodity

Christy Haragan, Principal Sales Engineer and Global GDPR Lead at MarkLogic, explains how she thinks companies need to structure and manage their data, and the issues they might face. Data Infrastructure is a topic in this month’s Business Chief and Haragan offers comment on the subject from 15 years’ worth of experience in the IT world.

How valuable a commodity is data?

Business is a science in today’s world, and data is what drives effective decisions – so data is arguably the most important asset any company has. We all know that the best product in the world is useless unless you can sell it. Selling it means knowing which market to address, what messaging to use and how to reach out to do so. Data is the most important component to answering these questions and achieving these goals.

Are some companies getting it wrong?

Where organisations fail is when they take decisions that aren’t backed by the right data. Knowing what data you need to have is thus equally critical. The challenge we face in the modern world is data overload - we have a paradox in that data is what enables us to sell effectively and operate efficiently, but that we have so much of it that it can seem impossible to find the right data.

See also:

How is ‘big data’ being used in the market these days?

The worlds of ‘Big Data’ and ‘Data Science’ are relatively new fields. However, the more traditional field of ‘Data Management’ has become more important in this new era of data-driven decision making. People have learnt that simply ‘dumping’ data into a ‘data lake’ will result in them ending up with a ‘swamp’. In fact, statistics have shown that data scientists, those highly skilled, highly expensive PhD hires, spend 80% of their time simply trying to manage data.

So why do people go down this route of dumping data together? Because data sits in silos. A medium-sized organisation will typically have 100 or so systems, while large organisations can have thousands. And this is to say nothing of the external data feeds organisations are looking to leverage. Systems were designed to solve a particular problem with a particular set of data, not to integrate different types of data together. Organisations spend billions each year in integration software, but traditional approaches are failing to keep pace with business and the rate of change of data.

What are the key steps organisations need to take to ensure they structure and manage data correctly?

Agile data management. What does this mean? Data management typically follows the same process of building a bridge:

  • Gather all requirements (the possible questions we would want to ask of the data)
  • Analyse these to build a data model (something that captures all the data points necessary to answer the questions)
  • Go to each data source or system that will be required to fill in the data model and look at their data models (which will have been built to serve the original purpose that data is used for)
  • Merge them all together to make them look like your target model

Only once all this is done can you actually start asking questions about your data. However, by then the business will have moved on, new data sources appeared, and people have gotten so bored that they’ll have likely opted for the ‘dump the data in one place’ approach previously mentioned.

Instead, an agile data management approach involves trying to answer one question at a time – working through just the data that supports that question, and then delivering those to our data scientists and letting them drive what further questions they wish to ask to improve their analysis. With data management improved, data scientists will be able to focus more of their time on gaining appropriate insight from data, which will give businesses quicker and better insights that will help them drive decisions that will help them grow.

Data management and compliance

As a final note to make, agile data management does not equate with process-free data management. Governance, security and control are all aspects of traditional data management that help enable the business to trust that the data is fit for purpose. As the data is brought together and treated in an agile data management fashion, part of the process in doing this should be adding metadata (information about the data). This meta-data would include details like:  Where did the data come from? Is this data personal data (necessary for GDPR compliance)? What quality requirements does this data have? Adding this metadata means that the data can be classified and brought under specific controls and processes to ensure it is held to the necessary standards and governance required by the business and by law.

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May 15, 2021

M&A activity key lever for future tech sector growth

Technology
dealmaking
EY
M&Aactivity
Kate Birch
2 min
With M&A activity in the technology sector soaring, dealmaking is likely to be the key lever for growth as businesses look to recover post-pandemic

Despite the continuing uncertainty of the pandemic, the tech sector has witnessed soaring dealmaking activity over the past year, rocketing in the second half of 2020, with the last quarter of 2020 a record one for M&A activity, and momentum continuing into 2021.

Dealmaking in tech sector soars in past year

And the latest figures bear this out with the number of technology M&A deals totalling US$208.44bn globally in Q1 2021, according to GlobalData. While the US holds top spot both in volume of deals (1034) and total value (US$140.61bn), Europe ranked next with 649 deals (US$44.49bn) with the UK continuing its reign as Europe’s biggest M&A market with 204 deals.

In particular, megadeals – those valued at US$5bn or more – soared in 2020 representing 59% of all global technology sector deal value in 2020, up from 47% in 2019, according to the latest edition of the EY Technology Global Capital Confidence Barometer.

This tech sector trend towards megadeals is backed up by EY’s CCB data, with 16% of tech sector respondents planning to pursue transformative deals valued at US$5bn or more in the near-term.

While technology deal activity “all but stopped at the beginning of 2020 after fluctuating between historic highs and lows, companies pivoted quickly and tech M&A exploded in the second half of the year”, says Barak Ravid, EY Global TMT Leader for Strategy and Transactions. 

M&A activity level for tech sector growth

Looking ahead to the future, technology executives are optimistic, with nearly half (47%) expecting profitability to fully rebound this year, according to CCB data, compared to 23% across all sectors, and with more than half (51%) planning to pursue M&A in the next year in order to sustain growth.

According to Ravid, M&A activity is increasingly becoming a key lever for growth as businesses look to recover.

“To position themselves for future revenue growth, tech companies are now adjusting their M&A strategy to focus more on a target’s business resilience, digital technology alignment and to gain market share through consolidation,” says Ravid.

However, with an increasingly competitive deal market and ongoing geopolitical tensions, the majority of tech execs expect to see more competition in the bidding process for assets over the next year, primarily from private capital.

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