In the current climate of uncertainty and change, companies must be able to adapt and respond to emerging market trends swiftly. Key to success is how well they use their data, not just measuring it, but using it to change strategy, drive product innovation and gain competitive advantage.
Jacyn Heavens, founder and chief executive of electronic point-of-sale company Epos Now, says: “The process of bringing new solutions to market more quickly and efficiently can be enhanced by analysing what has worked well and not so well in the past, and bringing that knowledge to bear in creating new product concepts and ultimately prototypes. This process must also be about gathering customer feedback and analysing to tweak and further enhance the solution moving forwards.”
Understanding the principles of using data to innovate is one thing, but are businesses putting them into practice? Research from Experian suggests senior executives are aware of the opportunities data provides to become more agile and more productive, but also highlights barriers such as limited budget (51 per cent) and volumes of data (47 per cent) that prevent businesses from exploiting these assets.
“A C-suite data champion representative will help,” says Experian’s data expert Rebecca Hennessy. “Only when the strategic value of data is realised at board level can businesses be in a better position to exploit data.”
For years the retail sector has lagged behind others in the use of technology to harvest data, but after a slow start the big data revolution is starting to emerge.
According to Dmitry Bagrov, UK managing director of technology consultancy DataArt, machine-learning using harvested big data is proving to be a Holy Grail for the retail industry’s marketing and strategy departments.
He says: “Firms that can better understand their customers’ unique desires and preferences will gain significant competitive advantage in deciding future strategy. For example, ‘scan as you shop’ devices are the easiest way to collect data in real time about how and why customers buy in a particular shop. Early adopters that apply machine-learning to harvested big data can build a far more detailed profile of their customers than those who lag behind.”
Data is one of the banking industry’s most valuable assets, yet most high street providers are still not using it smartly for upselling or improving customer services.
“If they want to keep the fintech challengers away from eating their lunch, heritage banks need to unleash and embrace the big data that is locked away in their 60-year-old systems,” says Nanda Kumar, president and chief executive of business software firm SunTec.
This could be achieved by using customer transactions to inform real-time offers, and create a personalised customer experience and additional value-add for an underappreciated market.
“When you book a holiday, for example, it’s likely that you also need travel insurance, money exchange and local offers at the destination,” says Mr Kumar. “Using their big data, banks have the power to help.”
Personal and predictive
The next big thing in travel will be better use of data to make travel more personalised and predictive in real time, but only if companies turn their data into consumer value in real time, says Scott Crawford, vice president of product at Expedia.
Data belongs to our customers and we give that data back to them in the most relevant, personalised way possible
“Through our scale, we have access to better travel data than anyone in the industry and we make our data work hard to create a better, personalised, customer experience,” he says. “However, we also want to give travellers the most relevant data for them, so we use pattern recognition that takes data from across our brands and delivers insights in a way that consumers find useful. Ultimately, our data belongs to our customers and we give that data back to them in the most relevant, personalised way possible.”
But companies shouldn’t get too hung up on the term big data. “It incites fear of information overload and cultivates the belief that working with data can be difficult, unwieldy and expensive,” says Edward Bass, co-founder and director of audience intelligence consultancy EntSight.
His advice is to break it down into three categories: multichannel, omnichannel and universal data. Multichannel data is what most organisations own and have pulled from internal systems such as customer relationship management systems, web and social, as well as consumer data from third parties. Omnichannel data, sourced from mobile apps, wearable technology and games consoles, is being used by companies to define user behaviour, while universal data comes from external sources and could include wider information that influences consumers such as weather and traffic conditions.
Ultimately, the most successful companies are those that ensure effective use of data throughout their enterprise. Dr Simon Tomlinson, data science business engagement manager at Lancaster University, concludes: “By combining and analysing their data sources, these companies have derived insights that deliver a real competitive advantage. In our work with industry partners, we have seen companies grasping the opportunity to gain a rich understanding of their customers, identify and eliminate production line issues, and instil a culture of continuous improvement based on real evidence.”
CASE STUDY: HALL HUNTER PARTNERSHIP
Hall Hunter Partnership (HHP) is a leading UK fruit grower, employing more than 2,000 workers across multiple sites in Berkshire, Surrey and West Sussex. The company gathers data from a number of disparate sources, including pickers and packers, soil and temperature sensors, and weather information, and consequently had always struggled to get a fast, accurate picture of the business, based on reliable data.
HHP business analyst Alex Gooi says: “We knew that a more efficient and accurate way to collate and analyse this data would enable the team to make better, data-driven decisions, eliminate guesswork, and allow us to identify and act quickly upon trends.”
The company decided to review its infrastructural stance on data management and build a data warehouse using WhereScape Data Warehouse automation. One of the long-term objectives was being able to compare identical varieties of fruit grown on similar crop systems between HHP farms to see how productivity, quality of fruit and yields compare.
Mr Gooi explains: “It is now easy to align fields within a common timeline reference without altering how data is originally recorded or stored. Having an accurate understanding of our stock positioning is also central to our crop and accounting forecast initiatives, particularly when it comes to our locally grown fruit. This enables us to calculate the total fruit tonnage harvested to date, and to determine with a degree of precision where our fields are within a harvest cycle and how aligned we are against our harvest programmes.”