The word collaboration refers to the action of working with someone to produce or create something. Collaboration is fundamental to teams working together successfully and delivering the best possible result in our output. Collaboration also leads to a much better work environment and culture within an organization.
The above definition accurately captures what we mean when we talk of collaborative data transformation workflows – namely, the act of bringing together diverse people in an organization, each with their own unique knowledge, to produce more comprehensive data transformation models.
Ventana Research, and leading research firm on BI and analytics, has devoted research on the use of collaboration in data and analytics this year. As organizations evolve their use of data for more in-depth insights that answer increasingly complex questions, they recognize that collaboration can help them get there faster and eliminate errors.
The core need for collaborative data transformation is to (a) help share the workload across the broader team, (b) allow different team members to bring their unique skills and knowledge, and (c) increase the analytics team’s knowledge of data. A greater understanding of the data transformation assets facilitates greater trust, brings new ideas on using the analytics, and generally produces faster insights with greater detail and accuracy.
A recent survey by the Datameer team showed that knowledge around analytics assets is dispersed in many locations across an organization. Collaborative data transformation can foster a shared understanding of an organization’s analytic assets.
A modern data transformation workflow embraces the fact that there are multiple personas involves in the analytics engineering process loosely grouped into two buckets: data engineers and data analysts/scientists. Each of these personas often has:
Data engineers tend to know more about the data itself – where it resides, how it is structured and formatted, and how to get it – and less about how the business uses the data. Their role is highly focused on managing data.
Data analysts and scientists know less about the data itself but have a complete understanding of how the business would use the data and how it would be incorporated into analytics. They may have varying technical skills but would prefer to spend more time on what they are good at – analysis – and less on coding data transformation.
A collaborative data transformation process for modern analytics engineering might look like:
This process is efficient and relatively self-service.
The different personas may have varying technical skills. Data engineers will be highly proficient in SQL and using data tools. Data analysts may have various degrees of SQL knowledge, but in general, maybe less technical and proficient in SQL. A data scientist may be technical because they need to program data science models but might not be efficient in programming data transformations in SQL.
To this end, Datameer DTaaS supports three different interfaces to support these multiple personas:
In addition, the different types of models (code, low-code, no-code) can be connected in a common data flow. This allows the different personas to contribute to the same workflow, using their best-suited modeling skills. Data engineers can create base models using SQL, then have analysts create their own tailored models using a no-code interface to graphically combine or reshape data or a low-code Excel-like interface where they can further refine the data or enrich it.
Data transformation collaboration begins and ends with teams working together in a common workspace to create the best models for their analytics. In shared workspaces, they can collaborate, share their knowledge, and share the modeling workload.
In Datameer DTaaS, shared workspaces are a foundational component. Data engineers, data analysts, business analysts, and data scientists can work together in shared workspaces creating and reusing models using the different tools – code, low-code, no-code. Beyond working together on modeling, the broader team can share knowledge about the workspace and models, including:
At the highest level, collaborative data transformation facilitates faster, analytics projects with fewer errors and re-work. At the next level, it delivers six key benefits:
Collaborative data transformation will increase the value the analytics community offers to the organization and bring more significant ROI to analytics programs.
Datameer DTaaS creates a highly communicative and collaborative workflow across the entire team of data engineers, analysts, and data scientists. Through multi-persona tools, shared workspaces, rich data documentation, tagging, commenting, custom properties, business metadata, and search and discovery, Datameer DTaaS gets the entire team working together, communicating, and sharing the workload. This enables faster, more agile data transformation processes and eliminates errors due to misinterpretation to ensure data transformation models get done right the first time.
"Jina ""ushirikiano"" linamaanisha kufanya kazi na mtu mwingine ili kutengeneza kitu." Ushirikiano ni muhimu kwa timu kufanya kazi pamoja kwa mafanikio na kutoa matokeo bora iwezekanavyo katika pato letu. Ushirikiano pia husababisha mazingira bora zaidi ya kazi na utamaduni ndani ya shirika. Ufafanuzi wa juu unachukua kwa usahihi kile tunachomaanisha tunapozungumza juu ya mtiririko wa kazi ya kubadilisha data ya ushirikiano - yaani, kitendo cha kuleta pamoja watu tofauti katika shirika, kila mmoja akiwa na maarifa yake mwenyewe ya kipekee, ili kutengeneza mifano ya kubadilisha data kamili zaidi. Ventana Research, kampuni inayoongoza ya utafiti wa BI na uchambuzi, imeweka utafiti juu ya matumizi ya ushirikiano katika data na uchambuzi mwaka huu. Kadiri mashirika yanavyoendelea kutumia data kwa ufahamu wa kina zaidi ambao hujibu maswali magumu zaidi, wanatambua kwamba ushirikiano unaweza kuwasaidia kufika hapo haraka na kuondoa makosa. Mahitaji ya msingi ya mabadiliko ya data ya ushirikiano ni (a) kusaidia kushiriki mzigo wa kazi katika timu pana, (b) kuruhusu wanachama tofauti wa timu kuleta ujuzi wao wa kipekee na maarifa, na (c) kuongeza maarifa ya timu ya uchambuzi ya data. Uelewa mkubwa wa mali ya mabadiliko ya data huwezesha uaminifu mkubwa, huleta mawazo mapya juu ya kutumia uchambuzi, na kwa ujumla hutoa ufahamu wa haraka na maelezo zaidi na usahihi. Uchunguzi wa hivi karibuni uliofanywa na timu ya Datameer ulionyesha kuwa maarifa kuhusu mali za uchambuzi yameenea katika maeneo mengi katika shirika. Ushirikiano data mabadiliko inaweza kukuza uelewa pamoja ya shirika la mali ya uchambuzi. Mtiririko wa kazi ya kisasa ya mabadiliko ya data inashughulikia ukweli kwamba kuna watu wengi wanaohusika katika mchakato wa uhandisi wa uchambuzi, ambao umegawanywa katika vikundi viwili: wahandisi wa data na wachambuzi wa data. Kila mmoja wa watu hawa mara nyingi ana: Wahandisi wa data huwa na kujua zaidi kuhusu data yenyewe - ambapo inakaa, jinsi ilivyoundwa na kupangwa, na jinsi ya kuipata - na chini kuhusu jinsi biashara inatumia data. Jukumu lao ni sana ililenga juu ya usimamizi wa data. Wataalamu wa data wanajua kidogo juu ya data yenyewe, lakini wana uelewa kamili wa jinsi biashara itatumia data na jinsi itajumuishwa katika uchambuzi. Wanaweza kuwa na ujuzi tofauti wa kiufundi lakini wangependa kutumia muda zaidi juu ya kile wanachofanya vizuri <unk> uchambuzi <unk> na chini juu ya kubadilisha data ya coding. Mchakato wa ushirikiano wa mabadiliko ya data kwa uhandisi wa kisasa wa uchambuzi unaweza kuonekana kama: Mchakato huu ni ufanisi na huduma ya kibinafsi. Watu tofauti-tofauti wanaweza kuwa na ustadi tofauti-tofauti wa kiufundi. Wahandisi wa data watakuwa na ujuzi mkubwa katika SQL na kutumia zana za data. Wataalamu wa data wanaweza kuwa na digrii mbalimbali za ujuzi wa SQL, lakini kwa ujumla, labda chini ya kiufundi na ujuzi katika SQL. Data Scientist inaweza kuwa ya kiufundi kwa sababu wanahitaji programu data sayansi mifano lakini inaweza kuwa na ufanisi katika programu data mabadiliko katika SQL. Kwa kusudi hili, Datameer DTaaS inasaidia interfaces tatu tofauti kusaidia persona hizi nyingi: Kwa kuongezea, aina tofauti za mifano (nambari, nambari ya chini, hakuna nambari) zinaweza kuunganishwa katika mtiririko wa data ya kawaida. Hii inaruhusu watu tofauti kuchangia mtiririko huo wa kazi, kwa kutumia ujuzi wao bora wa kuiga. Wataalamu wa data wanaweza kuunda mifano ya msingi kwa kutumia SQL, kisha kuwa na wachambuzi kuunda mifano yao wenyewe ya kawaida kwa kutumia interface ya no-code kwa graphically kuchanganya au kuunda tena data au interface ya chini ya Excel-kama ambapo wanaweza zaidi kusafisha data au kuimarisha. Ushirikiano wa mabadiliko ya data huanza na kumalizika na timu zinazofanya kazi pamoja katika nafasi ya kazi ya kawaida ili kuunda mifano bora kwa uchambuzi wao. Katika maeneo ya kazi ya pamoja, wanaweza kushirikiana, kushiriki maarifa yao, na kushiriki mzigo wa kazi ya modeling. Katika Datameer DTaaS, nafasi za kazi zilizoshirikiwa ni sehemu ya msingi. Wahandisi wa data, wachambuzi wa data, wachambuzi wa biashara, na wanasayansi wa data wanaweza kufanya kazi pamoja katika maeneo ya kazi ya pamoja kuunda na kutumia tena mifano kwa kutumia zana tofauti <unk> nambari, nambari ya chini, hakuna nambari. Zaidi ya kufanya kazi pamoja juu ya modeling, timu pana inaweza kushiriki maarifa kuhusu nafasi ya kazi na mifano, ikiwa ni pamoja na: Katika ngazi ya juu, ushirikiano data mabadiliko kuwezesha haraka, analytics miradi na makosa kidogo na rework. "Kama matokeo, mabadiliko ya data ya ushirikiano yatazidisha thamani ambayo jamii ya uchambuzi inatoa kwa shirika na kuleta ROI kubwa zaidi kwa programu za uchambuzi. """ Datameer DTaaS huunda mtiririko wa kazi wa mawasiliano na ushirikiano katika timu nzima ya wahandisi wa data, wachambuzi, na wanasayansi wa data. Kupitia zana za watu wengi, nafasi za kazi zilizoshirikiwa, nyaraka za data tajiri, kuweka alama, maoni, mali za kawaida, metadata ya biashara, na utafutaji na ugunduzi, Datameer DTaaS inapata timu nzima kufanya kazi pamoja, kuwasiliana, na kushiriki mzigo wa kazi. Hii inawezesha haraka, zaidi agile data mabadiliko ya taratibu na kuondoa makosa kutokana na misinterpretation kuhakikisha data mabadiliko ya mifano kupata kufanyika haki mara ya kwanza.