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Kirsty Garshong

Manager - UK

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Kirsty has seen both sides of recruitment at Harnham, originally staring in November 2014 as a graduate in the permanent Data team. After a year she moved into the contracts division and has helped to grow out the Data Warehousing and BI team from scratch. She is now a Managing Consultant and is working to grow out the niche verticals in her team within data storage, visualisation and big data.

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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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From Idea to Impact: How Charities Use Data

It’s that time of year again. As the festive season draws near and we pull together wish lists, many of us also begin to think about how we can give back. Given that the UK spent over £7 billion this Black Friday and Cyber Monday weekend, it’s not surprising that the idea of Giving Tuesday is becoming more and more popular. But with 160,000 registered charities in the UK alone, institutions are turning to data to find new ways to stand out and make a greater impact. Far from just running quarterly reports, charities are now utilising the insights they gain from data to inform their strategies, improve their services and plan for the future. IDEAS Given that not every charity is lucky enough to go viral with an Ice Bucket Challenge style video, there is a need to find other ways to stand out in such a crowded market. As such, many are looking to the data they have collected to help create a strategy. Macmillan Cancer Support, one the UK’s biggest charities, wanted to see more success from one of their main fundraisers, ‘The World’s Biggest Coffee Morning’. The event, which sees volunteers hold coffee and cake-fuelled gatherings across the country was revolutionised by data. By engaging with their database and researching what motivated fundraisers, they refocused their marketing around how the occasion could create an opportunity for people to meet up and chat, such as swapping ‘send for your free fundraising pack’ for ‘order your free coffee morning kit’. Whilst these amends may seem superficial, they had a major impact increasing funds raised from £15m to £20m. Some brands have taken this idea even further, using Data & Analytics tools to engage with potential donors. Homelessness charity Cyrenians’ data told them that there were a number of misconceptions about rough sleepers, including 15% of people believing that they were homeless by choice. To counter this they created an AI chatbot, named Alex, that allowed users to ask questions they may not have been comfortable asking a real person. Another charity using data tools to counter common misconceptions is Dyslexia Association. Their Moment of Dyslexia campaign saw them utilise facial recognition technology; the longer a person looked at their digital poster, the more jumbled up the words and letters became. By harnessing both insights and the technology made possible by data, they were able to offer an insight into what dyslexia is like for people who previously didn’t understand. INDIVIDUALS A big issue facing a number of charities is trust. Following a series of recent scandals, the public are more sceptical than ever of how charities are run, and their use of data is no exception. This ‘trust deficit’ has resulted in vast amount of potential donors staying away, with recent research highlighting that only 11% of people are willing to share their data with a charity, even if it means a better service. Whilst charities with effective Data Governance are able to use their vast amount of data to enhance those business, those who mismanage it are likely to suffer. Following a cyber-attack that exposed the data of over 400,000 donors, the British and Foreign Bible Society were fined £100,000. As hackers were able to enter the network by exploiting a weak password, this serves as a timely reminder that our data needs not only to be clean, but secure. Financial implications aside, improper data usage can also do irreversible damage to a charity’s reputation. St Mungo’s has faced criticism for passing information about migrant homeless people to the Home Office, putting them at risk of deportation. Whilst they were cleared of any wrongdoing by the ICO, this controversial use of data has had a negative impact on the charity’s image. With a decline in the number of people donating to charity overall, anything that can put people off further is bad news. IMPACT Whilst there is more demand than ever for charities to share their impact data, there is also more opportunity. With Lord Gus O’Donnell urging charities to make data an ‘organisation-wide priority’, many are going beyond publishing annual reports and fully embracing a culture shift. Youth charity Keyfund have been able to justify how the spend their funds based on their impact data. Having heard concerns from fundraisers regarding whether their leisure projects were effective they looked at the data they had gathered from the 6,000 young people they were helping. What they found was that not only were their leisure projects effective, they had an even more positive impact than their alternatives, particularly for those from the most deprived area. This allowed them to continue to support these programs and even increase funding where necessary. Going one step further are Street League, a charity that use sports programmes to tackle youth unemployment. Rather than share their impact data in quarterly, or even annual, reports they moved to real-time reporting. Interested parties can visit an ‘Online Impact Dashboard’ and see up-to-the-minute data about how the charity’s work is impacting the lives of the people it is trying to help. This not only allows for the most relevant data to be used strategically, but also supports the business holistically, gaining donor both attention and trust. To stand out in the charity sector institutions need to take advantage of data. Not only can this be used to generate campaigns and streamline services but, when used securely and transparently, it can help rebuild trust and offer a competitive edge. If you want to make the world a better place by harnessing and analysing data, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to see how we can help you.

by Kirsty Garshong

29. November 2018

Data skills are only part of the story

Make sure you tell the rest. You have probably heard that there is a shortage of highly skilled individuals in many specialised occupations. This is partially because the current marketplace is very positive, and business people in the UK are generally optimistic and upbeat. Optimism leads to expansion and with expansion comes new jobs. This is, of course, all very good news if you are looking for a better position or you are a graduate moving into the workplace. Right now, there is a lot of competition for good data analysts, and the result of this is increased salaries, better benefits and potentially a better chance of finding the job you want. A large part of our remit here at Harnham is to be a conduit between the jobseeker and the potential employer. In fairness to ourselves we are very good at getting people into the right role but there is still a lot you can do to help this process along. One of the small things you can do is recognise that as important and impressive as your data analysis skills are they are not the whole story of you. As you would expect in the analysis industries, there is a very heavy focus on skill set. Your skills are the door opener in many ways because without the appropriate skills the employer will simply not consider you. However, we need to be very careful of over-reliance on qualifications and skill set. Once over the first hurdle you will likely still be in a pool of candidates all of whom will have a similar skill set. If you look around our advice section, you will see hints on CV writing, interview techniques and several other useful preparation aids. (All of which will be helpful, because what you need to do now is stand out a little more.) On an even playing field a small thing can make a huge difference, so here are some general tips to help you stand out. Make sure you showcase soft skills such as teamwork and innovative practice. Employers are looking for you to be part of their business, and there is more to working than just being able to do the job. Make sure you find the opportunity to demonstrate you bring more than the tools of the trade. Demonstrate application. The employer is looking for you to demonstrate the application of your skill set not the skill set itself, - they already know you have that. This one should be relatively easy to show if you are switching jobs but can be difficult if you are a graduate. Your insightful, research-rich dissertation is probably very impressive, but most degrees will also include some practical implementation you can talk about as well. If you did a sandwich year or work experience then they should hear about it. Understand the wider marketplace. When you are applying for a specialised position, it is always good to demonstrate how this fits into the wider context of the employer's business and the general market. Have some examples memorised and make sure you know at least the bones of the employers marketplace, competitors and unique selling points. Following on from understanding the market is demonstrating that you see how the stakeholders will engage with your work. With more companies using data as an integral part of their ongoing strategy, a good candidate should be able to demonstrate that they can deliver not just the data but also strong, well-founded and derived, strategic recommendations that will drive stakeholder engagement with the analysis. It will become more and more a part of the role of the data analyst that they bring the data to life by demonstrating the results in a way that will engage the less technical audience. It is really very important that you are able to engage successfully with both internal and external stakeholders and translate your work into a suitable presentation language. Answering the ‘So What?’ – A common request from employers is to hire analysts with genuine enthusiasm for actionable insight and a clear appreciation of how it can be applied to business strategy. They obviously want to understand that you possess the required level of technical competence for the position, but this alone, often isn’t enough. Are you capable of answering the ‘so what’ questions that come about as a result of your analysis? You may be capable of producing complex statistical models, but ‘so what’ does that mean for the business? What recommendations can you, and have you made based on your analytical findings that have helped to improve business performance? Do you know the impact your analysis had? Make sure that you demonstrate your understanding of analytics in a broader commercial capacity, rather than purely focusing on your technical ability. Remember to simplify where needed. It is very easy to assume that everyone involved in the process will understand technical information or industry jargon and this may not always be the case. In a global business, for example, it is common for representatives of several areas to be involved in new appointments. Department managers may be hiring you as a specialist because they do not have that specialism in-house. Clearly do not explain everything in depth because there will be a general high level of knowledge in the room anyway, but remember that the more specialised the subject, the more you will need to explain. Standing out as an applicant is often just a matter of being sensitive to the needs of the employer and then pointing out where you meet those needs.

by Kirsty Garshong

21. May 2015

The future of DW's in the age of Big Data

Many companies are saddled with data warehouses that weren’t designed to handle big data, but they can evolve their data warehouses into “analytics warehouses” capable of processing structured and unstructured data.Enterprise data warehouses have reached a crossroads. Companies have spent millions of dollars designing, implementing, and updating them, but few organizations have realized the return they expected from their investments, according to Richard Solari, a director with Deloitte Consulting LLP’s Information Management service line.The disappointing ROI largely stems from an inherent inadequacy in data warehouses: They were designed to handle the kind of structured data stored in ERP systems, not the unstructured data from social media, mobile devices, Web traffic, and other sources now streaming into enterprises. By Solari’s estimate, 90 percent of the data warehouses he’s observed process just 20 percent of an enterprise’s data. Consequently, many enterprises have only been able to use their data warehouses for historical analysis and past performance reporting.The bottom line, says Solari: “Companies are using expensive infrastructure to generate back office reports.”Organizations’ prospects for obtaining an acceptable return from their data warehousing investments may continue to diminish as long as this infrastructure fails to keep pace with big data.The good news: Vendors are building new generations of data warehouses with advanced statistical capabilities for performing analytics and forecasting, according to Robert Stackowiak, Oracle Corp.’s vice president of information architecture and big data. They’re also improving integration with emerging platforms like Hadoop that process large volumes of unstructured data, he says.Because newer generations of data warehouses are designed to federate structured and unstructured data, they may provide enterprises with a 360-degree view of their operations and, with that broader perspective, the ability to make better decisions about the future, according to Solari.Companies running legacy data warehouses don’t have to junk their infrastructure and start anew. Solari says they can add capabilities to their existing data warehouse infrastructure that can allow it to grow into an “analytics warehouse.”“Data warehouses are going to look very different in five years, and organizations should begin preparing for that transition,” says Solari.Introducing the Analytics WarehouseFundamentally, the analytics warehouse functions as a central repository for an enterprise’s structured and unstructured data. In a traditional data warehousing architecture, structured data from ERP systems, CRM systems, file shares, and line of business applications is batch processed into the enterprise data warehouse using ETL (extract, transform, load) database processes. Software for running ad hoc queries and business intelligence systems take data from the warehouse environment, which may include operational data stores and data marts, to generate reports for users.The architecture for the analytics warehouse builds on the traditional data warehouse architecture in three primary ways:1. A distributed file system (like Hadoop) sits between source data systems and the data warehouse. It collects, aggregates, and processes huge volumes of unstructured data, and stages it for loading into the data warehouse.2. Structured and unstructured data from back end systems can be brought into the data warehouse in real- and near-real time.3. Engines that use statistical and predictive modeling techniques to perform data discovery, visualization, inductive and deductive reasoning, and real-time decision-making reside between the data warehouse and end users. These engines identify patterns in big data. They can also complement and feed traditional ad hoc querying tools and business intelligence applications. “In the past, companies couldn’t integrate these disparate technologies with the data warehouse because each technology required different file formats and data schemas,” says Stackowiak. “Today, you can integrate these technologies, and the result is that companies can access more of their data—not just the 20 percent from enterprise systems—and convert it into valuable, profitable information.”Companies interested in building out their traditional data warehouse infrastructures may consider starting with reporting, if they don’t already have reporting capabilities in place, suggests Solari. Then, they can begin integrating analytics technologies to their reporting framework.“When companies start bringing this data together and federating it inside a data warehouse, the total cost of ownership for the data warehouse may begin to go down while the ROI goes up,” says Solari. “The ability to integrate big data technologies, analytics technologies, back office systems, and traditional data warehouses has the potential to fundamentally change the economics of data warehousing for the better.” Click here for the article on the web.

by Kirsty Garshong

27. July 2013

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